Podcasts about Insilico Medicine

I had the chance to speak with Petrina Kamiya, Global Head of AI Platforms and VP at Insilico Medicine, as well as President of Insilico Medicine Canada. Insilico Medicine is what Petrina calls a “tech bio”—developing both AI platforms and therapeutic assets, with a flexible model licensing both. Their pharma.ai platform was created to address challenges in drug discovery all the way from target identification to the clinic. In just a few years, they've gone from having two core products to a suite of about 12, all built with a heavy emphasis on validation.When I think about AI in drug development, I think about all the failures in clinical trials. I've always wondered: are the molecules themselves to blame, or is the reason for so many failures rooted in the aspects that surround their testing—like patient selection, procedures, or trial design? Petrina confirmed that the two biggest reasons for failure are safety and efficacy. Many failures are turn out to be preclinical issues—either the wrong target was selected, or the molecule causes unintended side effects. AI and machine learning are being used to better predict both, by identifying high-confidence disease targets and designing safer molecules.But predicting toxicity is still a major challenge. There are models at every stage—from in silico predictions to in vitro and animal models—but each layer adds complexity, and good data to train AI models is notoriously hard to come by. A lot of data around failed molecules never makes it into the public domain because it's proprietary. That means valuable insights about toxicity are often lost, though some substructures known to be problematic are at least captured in public databases. I realize that companies need a return on their investment and even failure data has competitive value. But you have to wonder how much money is wasted chasing dead ends that could have been avoided.The other question I always have is about the mechanics of drug binding. Most approaches focus on the active site—the orthosteric site—where the protein normally interacts with its natural ligand. I asked about the possibility of other strategies like allosteric binding (where a drug binds somewhere else on the protein to inhibit function). Petrina validated that idea along with degraders, which are molecules designed to bring a protein into contact with the cellular machinery that destroys it. These newer modalities, including molecular glues, offer ways to selectively disable problem proteins without relying on traditional binding.Nothing is straightforward. Allosteric sites can offer greater selectivity, which could reduce toxicity. But finding those sites is incredibly difficult because proteins are dynamic and mobile. It's not just about structure; it's about motion within the protein itself and context.The body's backup systems—redundant pathways, mutations, and rescue mechanisms—can undermine even well-designed drugs. This is especially relevant in oncology. Proteins like KRAS have so many variants that it's not enough to design one effective inhibitor—you often need a panel of drugs to address different mutations. Petrina noted that the human body has many fallback mechanisms, which makes targeting disease pathways more difficult but also explains why drugs that seem perfect in vitro don't always deliver in the clinic.Not subscribed? Let's fix that. No spam, just good content wherever I find it.Getting back to clinical trials, AI is mostly being applied operationally right now—to optimize patient selection, identify clinical sites with the right patient profiles, and monitor for trial reporting issues. The big advantage is in stratifying patients to improve the signal-to-noise ratio. As Petrina noted, sometimes a drug works for a subset of patients, but that signal is lost in the broader trial data. That resonated with my previous interview with Kurt Mussina who used AI to identify ideal site locations based on logistics and patient demographics—a very practical, high-impact use of the technology.What if we could recover some therapies that have previously failed because it wasn't tested on the right people? AI could help salvage and reposition those compounds by uncovering hidden signals in the data. You have to believe that improvements in AI will find a few lost nuggets—digging back through data with better tools to find value that's already there.Developing therapies aren't the only application for new molecule discovery. Insilico is also working with companies in the herbicide space, and as Petrina explained, discovering herbicides isn't all that different from designing drugs for people. You still need target specificity, safety, and cost-efficiency—but at an even greater scale of production. If people or animals are exposed, or if the herbicide lingers in the environment, it has to meet a high safety bar.The unique challenge here is complexity and scale. It comes down to economics. We may spare no expense to extend a human life with doses in the milligram range. In agriculture, you're looking for a simple compound that is cheap, can be produced in massive quantities, and can be stored in almost any conditions. It's a new set of constraints.AI in discovery isn't about magic. It's about building better foundations—more accurate models, more validated data, and more thoughtful decision-making—to improve every step from discovery to clinical success.Your deepest insights are your best branding. I'd love to help you share them. Chat with me about custom content for your life science brand. Or visit my website. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cclifescience.substack.com

人工智慧（AI）如何改變藥物開發？藥物開發從臨床前研究到臨床試驗，通常需要超過十年時間和數十億美元的投資，卻仍有高達九成的藥物在臨床試驗中失敗。這些挑戰，現在 AI 正在各種方面掀起革命。在這系列節目中，我們將探討 AI 在藥物開發領域中的應用，並介紹在這個快速發展的領域中工作的專家。 本集為《AI 藥物開發系列》的第二集。在本集節目中，我們特別邀請到了陽明交大的兼任助理教授林彥竹，他同時也是英科智能 Insilico Medicine 台灣區的前 CEO*。他將分享他從臺大藥學院畢業後，如何跨足至瑞士聯邦理工學院，並進入英科智能深耕 AI 藥物開發的豐富經歷，以及他對於 AI 在生醫領域未來發展的看法。 此外，我們也探討英科智能的平台如何加速藥物篩選的過程，從而大幅縮短從實驗室到臨床的時間。希望大家將能更清楚地了解 AI 如何改變傳統藥物研發流程，以及這場科技革命如何影響生醫領域的未來。 *註: 講者受訪時仍任職於英科智能  

Send us a textEli Mohamad is a prominent figure in the biotech, space, and AI industries who has co-founded several successful startups and has a real passion for groundbreaking ventures that focus on the development of futuristic technologies.Currently as a Core Team Member at CryoDAO ( https://www.cryodao.org/ ), a decentralized organization focused on sourcing and funding research in cryopreservation, Eli continues to work at the forefront of innovative technologies and applies his extensive experience in biotechnology and innovative projects to advance novel cryopreservation technologies and their various applications, from critical tissue and organ preservation, to cryo-sleep and suspended animation for space exploration.Eli has also been involved in setting up another decentralized organization called HydraDAO ( https://hydradao.org/ ) which is focused on funding and incubating biological replacement research to significantly extend human lifespan and will be looking at everything from Limb Regeneration, Organ Bioprinting and Xenotransplantation, to  Progressive Brain Replacement,  head/brain transplants, and even whole body replacement via non sentient cloning.Throughout his career, Eli has held various leadership positions in cutting-edge companies including as Co-founder and Chief Business Officer at X-Therma Inc., a company focused on complex tissue preservation; Advisor and Chief Business Officer at Rimac Automobili, working on high-performance electric vehicles; CFO/CBO at Insilico Medicine, Inc., as well as Co-founder of Organ Preservation Alliance, a non-profit organization dedicated to the future of organ banking, Orphidia Ltd., a medical diagnostics company, and Walkmore, a data science company.Eli holds a Master's degree in Science, Technology and Innovation Management from the University of Sussex. He is also an alumnus of Singularity University's GSP (Global start up) 2012 program.  Kai Micah Mills ( https://kaimicahmills.com/ ) is the visionary founder of Cryopets ( https://cryopets.com/ ), a company focused on advancing cryopreservation technologies for pets. Cryopets was founded in 2023 with the aim of revolutionizing the field of cryopreservation. Starting with pets, the company is innovating on existing methods to create reliable and accessible cryopreservation services. Cryopets aspires to make cryopreservation available in local veterinary clinics and, eventually, in human hospitals.With a deep passion for defeating death, Kai is driven by the belief that true progress in longevity science must be bold and unapologetic. He has received backing from the prestigious Thiel Fellowship and is a recognized thought leader in the radical life extension community.Kai is a co-founder of CryoDAO and HydraDao.Important Episode Link - Vitalist Bay - https://www.vitalistbay.com/#EliMohamad #KaiMicahMills #Longevity #Immortality #LifeExtension #ProgressiveBrainReplacement #Cryopreservation #OrganPreservation #Bioprinting #VitalistBay #Vitalism #AdamGries #NathanCheng #HydraDAO #CryoDAO #DeSci #Cryonics #Regeneration #Cryoprotectants #LimbRegeneration #Xenotransplantation #HeadTransplant #BrainTransplant #SpinalCordInjury #SergioCanavero #RenXiaoping #ClonedNonSentientBodies #Cloning #ArtificialWombs #Cryosleep #Crypto #ProgressPotentialAndPossibilities #IraPastor #Podcast #Podcaster #ViralPodcast #STEM #Innovation #Technology #Science #ResearchSupport the show

While our podcast takes a brief break, Labiotech invites you to enjoy some of our favorite episodes. We will return with a brand-new episode on August 16, 2024! Have an awesome summer!Artificial intelligence (AI) is certainly in the news constantly; however, it's been used in drug discovery for some time.A new collaboration between artificial intelligence drug discovery company Insilico Medicine and University of Toronto biochemist and molecular geneticist Igor Stagljar will test AI-designed molecules against "undruggable" cancer targets. The research will test 15 to 20 undruggable targets - but are they undruggable? And how does AI work in the drug discovery process?This week, we have a conversation with Kyle Tretina, Alliance Manager of AI Platforms at Insilico Medicine, on a wide range of subjects including drug discovery, undruggable targets, the collaboration with the University of Toronto, and more.00:58-05:11: About Insilico Medicine05:11-06:09: Why is AI in the news?06:09-07:39: Helping people through AI07:39-09:10: What is Insilico Medicine doing with AI?09:10-10:15: Does Insilico Medicine take drugs from idea to trials?10:15-11:32: How do your partnerships come about?11:32-19:34: How does drug development start with AI?19:34-24:43: Can AI address undruggable targets?24:43-25:05: What do you need to do after finding a potential drug?25:05-27:57: Can quantum computing aid drug development?27:57-30:13: How can AI help reduce costs and save time?30:13-32:56: What is your partnership with the University of Toronto?32:56-36:24: What is the timescale for introducing drugs from AI?36:24-37:29: What conditions are you working on?Interested in being a sponsor of an episode of our podcast? Discover how you can get involved here! Stay updated by subscribing to our newsletter

Hello folks and welcome to This Is Robotics. I'm your host and fellow companion, Tom Green.The last half of 2024 is upon us, robotics-driven automation is in rapid ascendancy once again, especially now since 2024 is showing how robotics engages with GenAI, and how prompt engineering is significantly increasing the ease of adoption for robots everywhere. Last month, we gave you a longish one-hour show, which was necessary for it was meant to support my keynote address at SuperTechFT in San Francisco.  If you have yet to listen to it, it's Episode # 31 and deals with how quickly computer code has capitulated to prompt engineering…and why. Plus, the new breed of workers on the rise who are being hailed as the “New Collar” generation of workers.This month, we are listening to our global fans for feedback. We have a global fan base in 68 countries according to Buzzsprout stats. A fangirl Celina from the Philippines wants us to reprise a woman's show. Specifically, the rise of Alice Zhang (Verge Genomics) and her pursuit of answers to neurodegenerative diseases like Alzheimer's, Parkinson's, and ALS. Thank you, Celina for also pointing out how this story highlights how human insight creates the technical challenge and how LLMs are then employed to reveal a way forward for bio research. For Alice, it was robotics and LLMs cracking the code for ALS.During Alice's piece, she laments how broken bio research is and why. Which leads to our second fan request from Martin in Augsburg, Germany, who was fascinated with robotics in bio labs working with AI in what he calls Pharma 4.0. New drug research and discovery companies with strange, new names like Recursion Pharmaceuticals, Arctoris, Insitro, Relay Therapeutics, and Insilico Medicine are forging the way. Martin, good pick.We lead off this month humankinds almost innate fascination and attraction to humanoid robots. Why is that? We let a half dozen experts offer up some truly interesting insights and theories on just why that is. Those insights are wrapped up in a show about human attraction to robots where we commemorate National Kiss & Make up Day which is coming up in August.Okay, strap on your earphones or pop in your earbuds, which Buzzsprout tells us 3,000 people do daily worldwide to listen to This Is Robotics. We're thrilled you can join us today. Thanks and welcome.https://asianroboticsreview.com/home591-html

Dive into the groundbreaking approaches of companies like VivoDyne, Seer, Ordaos, Insilico Medicine, and Alchemab, who are pushing boundaries in drug R&D through microfluidic bioengineering, proteome mapping, AI-driven drug design, and resilient antibody discovery.

This week we get into AI in health care, generative and personalized medicine, the cure for cancer and why CoVID helped supercharge that and how AI is helping us live longer, healthier lives. Alex Zhavoronkov, the founder of AI-MedTech player Insilico joins us on TF to talk how health care is going to radically change over the next decade or two. An amazing deep dive. Don't miss it! Future health care trends with Alex Zhavoronkov Alex Zhavoronkov, PhD, is the founder and CEO of Insilico Medicine (insilico.com), a leading clinical-stage biotechnology company developing next-generation artificial intelligence and robotics platforms for drug discovery. He is also the founder and Chief Longevity Officer of Deep Longevity, Inc, a spin-off of Insilico Medicine developing a broad range of artificial intelligence-based biomarkers of aging and longevity servicing healthcare providers and life insurance industry. In 2020 Deep Longevity was acquired by Endurance Longevity (HK: 0575).Since 2014 he has invented critical technologies in the field of generative artificial intelligence and reinforcement learning (RL) for the generation of novel molecular structures with the desired properties and generation of synthetic biological and patient data. He also pioneered the applications of deep learning technologies for the prediction of human biological age using multiple data types, transfer learning from aging into disease, target identification, and signaling pathway modeling. Under his leadership, Insilico raised over $415 million in multiple rounds from expert investors, opened R&D centers in six countries or regions, partnered with multiple pharmaceutical, biotechnology, and academic institutions, nominated 11 preclinical candidates, and entered human clinical trials with AI-discovered novel target and AI-designed novel molecule.Prior to founding Insilico, he worked in senior roles at ATI Technologies (GPU company acquired by AMD in 2006), NeuroG Neuroinformatics, Biogerontology Research Foundation. Since 2012 he published over 160 peer-reviewed research papers, and 2 books including "The Ageless Generation: How Biomedical Advances Will Transform the Global Economy" (Macmillan, 2013). He serves on the advisory or editorial boards of Trends in Molecular Medicine, Aging Research Reviews, Aging, Frontiers in Genetics, and founded and co-chairs the Annual Aging Research, Drug Discovery and AI Forum (9th annual in 2022), the world's largest event on aging in the pharmaceutical industry. He did his two bachelor degrees at Queen's University in Canada, masters in biotechnology at Johns Hopkins, and PhD in biophysics at MSU. He is the adjunct professor of artificial intelligence at the Buck Institute for Research on Aging. See more podcasts here, and see more about The Futurists here.

This week we get into AI in health care, generative and personalized medicine, the cure for cancer and why CoVID helped supercharge that and how AI is helping us live longer, healthier lives. Alex Zhavoronkov, the founder of AI-MedTech player Insilico joins us on TF to talk how health care is going to radically change over the next decade or two. An amazing deep dive. Don't miss it! Alex Zhavoronkov, PhD, is the founder and CEO of Insilico Medicine (insilico.com), a leading clinical-stage biotechnology company developing next-generation artificial intelligence and robotics platforms for drug discovery. He is also the founder and Chief Longevity Officer of Deep Longevity, Inc, a spin-off of Insilico Medicine developing a broad range of artificial intelligence-based biomarkers of aging and longevity servicing healthcare providers and life insurance industry. In 2020 Deep Longevity was acquired by Endurance Longevity (HK: 0575).Since 2014 he has invented critical technologies in the field of generative artificial intelligence and reinforcement learning (RL) for the generation of novel molecular structures with the desired properties and generation of synthetic biological and patient data. He also pioneered the applications of deep learning technologies for the prediction of human biological age using multiple data types, transfer learning from aging into disease, target identification, and signaling pathway modeling. Under his leadership, Insilico raised over $415 million in multiple rounds from expert investors, opened R&D centers in six countries or regions, partnered with multiple pharmaceutical, biotechnology, and academic institutions, nominated 11 preclinical candidates, and entered human clinical trials with AI-discovered novel target and AI-designed novel molecule.Prior to founding Insilico, he worked in senior roles at ATI Technologies (GPU company acquired by AMD in 2006), NeuroG Neuroinformatics, Biogerontology Research Foundation. Since 2012 he published over 160 peer-reviewed research papers, and 2 books including "The Ageless Generation: How Biomedical Advances Will Transform the Global Economy" (Macmillan, 2013). He serves on the advisory or editorial boards of Trends in Molecular Medicine, Aging Research Reviews, Aging, Frontiers in Genetics, and founded and co-chairs the Annual Aging Research, Drug Discovery and AI Forum (9th annual in 2022), the world's largest event on aging in the pharmaceutical industry. He did his two bachelor degrees at Queen's University in Canada, masters in biotechnology at Johns Hopkins, and PhD in biophysics at MSU. He is the adjunct professor of artificial intelligence at the Buck Institute for Research on Aging.

(00:03:07) Regulierungsversuch von TikTok in USA https://www.nytimes.com/2024/03/08/us/politics/biden-tiktok.html https://www.nytimes.com/2024/03/07/business/tiktok-phone-calls-congress.html https://www.tagesschau.de/wirtschaft/technologie/tiktok-instagram-meta-wachstum-user-reels-influencer-100.html (00:09:40) Instagram überholt TikTok als die meist heruntergeladene App https://www.tagesschau.de/wirtschaft/technologie/tiktok-instagram-meta-wachstum-user-reels-influencer-100.html https://www.campaignlive.com/article/meta-enjoys-record-adspend-tiktok-faces-39-engagement-decline/1863539 https://www.theverge.com/2024/3/5/24091278/congress-tiktok-bytedance-ban-divest-bill https://www.reuters.com/technology/new-push-congress-ban-tiktok-or-force-chinese-divestiture-gains-steam-2024-03-07/ https://nymag.com/intelligencer/2024/01/what-i-learned-selling-a-used-pencil-on-tiktok-shop.html (00:21:17) Wie Apple mit “Malicious compliance” versucht die europäische Regulierung zu umgehen https://www.theverge.com/2024/3/4/24005938/european-commission-antitrust-apple-investigation-anti-steering-rules-app-developers https://www.nytimes.com/2024/03/06/technology/apple-epic-games-feud.html https://www.notebookcheck.com/1-8-Milliarden-Euro-Apple-beschimpft-Spotify-nach-EU-Strafe.809829.0.html https://www.cnbc.com/2024/03/08/apple-reverses-course-approves-epic-games-for-app-store-in-europe.html https://developer.apple.com/security/complying-with-the-dma.pdf https://archive.ph/2024.03.08-205632/https://www.wsj.com/tech/apple-reverses-ban-on-fortnite-maker-in-eu-ceadbfbc (00:33:17) OpenAI äußert sich zu Musks Klage https://openai.com/blog/openai-elon-musk (00:40:09) Neue Modelle bei Anthropic und Inflection AI https://youtu.be/ReO2CWBpUYk?si=rG3NRfo2lVCO19Ad https://pi.ai/discover Außerdem: TripoSR von stability.ai - 3D rendering von Objekten Perplexity AI unicorn, Adobe Firefly auf dem Smartphone (00:41:41) Der KI generierte und entdeckte Wirkstoff von Insilico Medicine erreicht Phase II der klinischen Studien https://venturebeat.com/ai/insilico-medicine-unveils-first-ai-generated-and-ai-discovered-drug-in-new-paper/ Künstliche Intelligenz auf der Spur des Lebens https://www.nytimes.com/2024/03/10/science/ai-learning-biology.html (00:55:16) Lamest spying of all times: Google Mitarbeiter spioniert für China und lädt die geklauten Informationen in das persönliche Google Drive hoch… https://www.businessinsider.com/google-engineer-ran-secret-startup-china-stealing-ai-tech-doj-2024-3 (01:00:33) Buchempfehlung Burn Book von Kara Swisher

In 2023, Insilico Medicine—a biotech company developing medications with a heavy reliance on AI—used AI to develop an experimental drug for the incurable lung disease idiopathic pulmonary fibrosis. The treatment is in mid-stage trials in the US and China, with some results expected in early 2025. Biotech is one of the fields that has been using generative AI for years, even before ChatGPT brought the technology to public view. Latest technology is essential in drug development. However, the convergence of digital health and pharma seems less clear. Digital health apps started gaining popularity around 2015, and at that time, it seemed all pharma companies were trying to figure out what they could gain from apps, so they financed accelerators and incubators one after the other. We've seen many ideas about how Pharma should or could use digital health. In the last few years, there have been many notorious cases when partnerships failed—a seemingly unicorn, Proteus, which designed digital sensors-equipped pills, went bankrupt in 2019 after Otsuka Pharmaceuticals pulled out of a funding round. Pear Therapeutics, the guiding star in the DTx space and the leader in FDA-cleared prescription digital therapeutics, partnered with Novartis, but in the end, the company filed for bankruptcy in 2023. So where is Pharma in relation to digital health and digital therapeutics? In this episode, Amir Lahav shares his thoughts about the impact of AI on biotech, the state of decentralized clinical trials, and the potential of technology for improved drug development, clinical trials, and patient responses. Newsletter: https://fodh.substack.com/ www.facesofdigitalhealth.com Show notes: [00:02:00] The Convergence of Digital Health and Pharma Discussion on the role of digital health apps in pharmaceuticals. The rise and fall of pharma and tech company partnerships, with examples like Proteus and Peer Therapeutics. [00:06:00] AI Trends in Biotech and Pharma [00:08:00] Enhancing Clinical Trials with AI and continuous patient monitoring [00:10:00] The Importance of Data in Clinical Trials [00:12:00] The Reality of Oncology Trials and Endpoints [00:14:00] Quality of Life in Medicine as the Endpoint [00:16:00] The Rise of Decentralized Clinical Trials [00:18:00] Pharma's Evolving Digital Health Strategies [00:22:00] Impact on Digital Health Industry [00:24:00] Collaboration and Sharing Knowledge in the Pharma Industry [00:26:00] The need for long-term investment and strategic piloting of digital health solutions [00:28:00] What Inspires in Pharma and Biotech in Personalized Treatments [00:30:00] The State of Precision Medicine and Targeted Therapies [00:34:00] The Role of Pharmacogenomics [00:36:00] Anticipations for 2024 and Beyond

BUFFALO, NY- February 20, 2024 – A new #research paper was #published in Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 16, Issue 3, entitled, “Defining the progeria phenome.” Progeroid disorders are a heterogenous group of rare and complex hereditary syndromes presenting with pleiotropic phenotypes associated with normal aging. Due to the large variation in clinical presentation the diseases pose a diagnostic challenge for clinicians which consequently restricts medical research. In this new study, researchers Cecilie Worm, Maya Elena Ramirez Schambye, Garik V. Mkrtchyan, Alexander Veviorskiy, Anastasia Shneyderman, Ivan V. Ozerov, Alex Zhavoronkov, Daniela Bakula, and Morten Scheibye-Knudsen from the University of Copenhagen and Insilico Medicine aimed to accommodate this challenge by compiling a list of known progeroid syndromes and calculating the mean prevalence of their associated phenotypes, defining what they term the ‘progeria phenome'. “In this study, we have utilized phenome explorations to define the phenotypes associated with progerias and to develop tools to diagnose patients and identify new progeroid syndromes.” The data were used to train a support vector machine that is available at https://www.mitodb.com and able to classify progerias based on phenotypes. Furthermore, this allowed the researchers to investigate the correlation of progeroid syndromes and syndromes with various pathogenesis using hierarchical clustering algorithms and disease networks. They detected that ataxia-telangiectasia like disorder 2, spastic paraplegia 49 and Meier-Gorlin syndrome display strong association to progeroid syndromes, thereby implying that the syndromes are previously unrecognized progerias. “In conclusion, our study has provided tools to evaluate the likelihood of a syndrome or patient being progeroid. This is a considerable step forward in our understanding of what constitutes a premature aging disorder and how to diagnose them.” DOI - https://doi.org/10.18632/aging.205537 Corresponding author - Morten Scheibye-Knudsen - mscheibye@sund.ku.dk Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.205537 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, progeria, premature aging, phenome, clinical phenotype About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

Artificial intelligence (AI) is certainly in the news constantly; however, it's been used in drug discovery for some time.A new collaboration between artificial intelligence drug discovery company Insilico Medicine and University of Toronto biochemist and molecular geneticist Igor Stagljar will test AI-designed molecules against "undruggable" cancer targets. The research will test 15 to 20 undruggable targets - but are they undruggable? And how does AI work in the drug discovery process?This week, we have a conversation with Kyle Tretina, Alliance Manager of AI Platforms at Insilico Medicine, on a wide range of subjects including drug discovery, undruggable targets, the collaboration with the University of Toronto, and more.00:58-05:11: About Insilico Medicine05:11-06:09: Why is AI in the news?06:09-07:39: Helping people through AI07:39-09:10: What is Insilico Medicine doing with AI?09:10-10:15: Does Insilico Medicine take drugs from idea to trials?10:15-11:32: How do your partnerships come about?11:32-19:34: How does drug development start with AI?19:34-24:43: Can AI address undruggable targets?24:43-25:05: What do you need to do after finding a potential drug?25:05-27:57: Can quantum computing aid drug development?27:57-30:13: How can AI help reduce costs and save time?30:13-32:56: What is your partnership with the University of Toronto?32:56-36:24: What is the timescale for introducing drugs from AI?36:24-37:29: What conditions are you working on?Interested in being a sponsor of an episode of our podcast? Discover how you can get involved here! Stay updated by subscribing to our newsletter

New Year, new podcast format! In 2024's first episode of the Scope of Things, host Deborah Borfitz gives you the latest news from the clinical trials industry, including AI's role in the creation of digital twins, the obesity epidemic and the drugs intended to combat it, improving diversity in trial candidates, and more. We also have expert advice from Ward Lemaire, VP Head of Data Management and Central Monitoring at J&J Innovative Medicine, and Dan Hydes, Co-Founder and CEO at IgniteData, on SCOPE's new Startup Pitch Competiton, and how start-up companies can meet multiple demands and needs, direct their limited resources, and keep the innovation train going. New Advisory Board Ken Getz podcast episode  Ramita Tandon podcast episode   News Roundup AI and Chatbots Belong.Life Microsoft's Trial Matcher UF and NVIDIA's GatorTronGPT Genentech's smart digital assistants Digital Twins Pan-European project simulating stroke treatments Obesity Clinical Trials JAMA paper on unintended consequences of weight-loss drugs   FDA's Ozempic label change Physicians in Europe preferentially recommend lifestyle changes  New areas of interest Trial begins for Insilico Medicine's latest AI-discovered drug Brain metastases studies in Italy and Spain  Drug repurposing study in Australia for type 1 diabetes Anti-aging TAME trial of Metformin to start Pharmacy Research Organization RxE2 Platform for independent community pharmacists launches Gerald Finken podcast episode Amplifying the patient voice FDA's patient- focuse

Trong những năm gần đây, ngành công nghiệp dược phẩm, trong đó có ngành dược phẩm của Pháp, sử dụng ngày càng nhiều các ứng dụng trí tuệ nhân tạo, và nhờ vậy đang đẩy nhanh tiến trình bào chế các loại thuốc mới.  Vào tháng 4 năm ngoái, Drugs for Neglected Diseases Initiative, một tổ chức phi chính phủ châu Âu, đã khởi động một dự án dùng trí tuệ nhân tạo để tìm thuốc trị bệnh sốt xuất huyết Dengue, trong khuôn khổ một đối tác với BenevolentAI, một công ty Anh Quốc chuyên phát triển các phân tử mới nhờ vào trí tuệ nhân tạo. Công ty khởi nghiệp đi tiên phongThật ra thì trước đó, vào đầu năm 2020, Exscientia, một công ty khởi nghiệp Scotland đã cùng với viện bào chế Sumitomo Dainippon của Nhật chế ra một phân tử đầu tiên hoàn toàn bằng trí tuệ nhân tạo.Còn công ty công nghệ y tế Genetika+ của Israel, do chuyên gia về khoa học thần kinh Cohen Solal đồng sáng lập vào năm 2018, đã nghiên cứu việc kết hợp các công nghệ mới nhất về tế bào gốc với một ứng dụng trí tuệ nhân tạo để giúp xác định thuốc chống trầm cảm thích ứng tốt nhất với bệnh nhân, nhằm tránh các phản ứng phụ và để bảo đảm thuốc có tác dụng hiệu quả nhất có thể. Genetika+ hy vọng là nhu cầu về công nghệ của họ sẽ rất lớn, bởi vì theo Tổ chức Y tế Thế giới, trên toàn thế giới hiện có hơn 280 triệu người mắc chứng trầm cảm. Theo thẩm định có đến 2 phần 3 toa thuốc ban đầu kê cho các bệnh nhân trầm cảm là không có tác dụng gì, vì không thích ứng với bệnh nhân. Công ty khởi nghiệp của Pháp Iktos, được thành lập vào năm 2016, nay chỉ sử dụng trí tuệ nhân tạo để đối chiếu các khối dữ liệu y tế với một vận tốc mà không bộ não con người nào có thể đạt được. Theo lời Yann Gaston-Mathé, lãnh đạo công ty mà ông là người đồng sáng lập, trí tuệ nhân tạo được sử dụng để “khai thác các dữ liệu hiện có để chế tạo những phân tử mới tốt hơn và nhanh hơn”. Ê kíp của ông đã sử dụng một cơ sở dữ liệu của 100 triệu phân tử và từ cơ sở dữ liệu này họ đã “huấn luyện” một mô hình biết tự động tạo ra những phân tử mới. Iktos thậm chí còn lập một nền tảng nghiên cứu chế tạo phân tử bằng trí tuệ nhân tạo cho các công ty dược phẩm sử dụng dưới hình thức thuê bao. Một trong những công ty đầu tiên sử dụng nền tảng mang tên Saas của Iktos là Kissei, một hãng dược phẩm lớn của Nhật, đã được thành lập từ cách đây gần 75 năm. Nhờ đối tác ký với Iktos, Kissei sẽ tiết kiệm rất nhiều thời gian trong tiến trình khám phá và phát triển các loại thuốc mới.Công ty Insilico Medicine tại Hồng Kông cũng đang sử dụng trí tuệ nhân tạo để đẩy nhanh việc phát hiện các loại thuốc mới. Ông Alex Zhavoronkov, đồng sáng lập viên và hiện là tổng giám đốc công ty, giải thích: “ Nền tảng trí tuệ nhân tạo của chúng tôi có thể xác định các loại thuốc có thể được tái sử dụng, bào chế các thuốc mới cho những mục tiêu phân tử (cible ) đã được biết, hay tìm ra mục tiêu phân tử mới và chế ra những phân tử mới."Các đại tập đoàn nhập cuộcTrong vài năm trở lại đây, các đại tập đoàn trong ngành dược phẩm cũng đã đầu tư ngày càng nhiều vào các ứng dụng trí tuệ nhân tạo. Ví dụ như tập đoàn Mỹ Bristol-Myers Squibb vào năm 2021 đã ký thỏa thuận hợp tác với Exscientia, dự trù sẽ cấp tổng cộng hơn 1 tỷ đôla để sử dụng các dịch vụ của công ty này.Các đại tập đoàn công nghệ số cũng nhập cuộc: Microsoft vào năm 2019 đã thông báo hợp tác với tập đoàn dược phẩm Novartis của Thụy Sĩ để đẩy nhanh việc sử dụng trí tuệ nhân tạo trong mỗi giai đoạn của tiến trình bào chế một loại thuốc mới. Thỏa thuận giữa hai tập đoàn này có thời hạn là 5 năm.Tập đoàn dược phẩm Pháp Sanofi cũng đang đẩy mạnh việc sử dụng trí tuệ nhân tạo không chỉ trong khâu nghiên cứu mà cả trong khâu thương mại hóa. Trong bản thông cáo được công bố ngày 13/06/2023 nhân triển lãm VivaTech Paris 2023, ông Paul Hudson, tổng giám đốc Sanofi, cho biết tham vọng của họ là trở thành tập đoàn dược phẩm đầu tiên sử dụng trí tuệ nhân tạo ở quy mô lớn, trang bị cho các nhân viên của họ những công cụ và công nghệ giúp họ lấy những quyết định tốt nhất. Theo lời ông Hudson, trí tuệ nhân tạo và khoa học dữ liệu đã trợ giúp rất nhiều cho Sanofi trong các lĩnh vực như phát hiện các thuốc mới, cải thiện hiệu quả của các thử nghiệm lâm sàng, sản xuất và cung ứng thuốc và vac-xin. Trả lời phỏng vấn RFI tại triển lãm VivaTech, ông Emmanuel Frenehard, đặc trách các sản phẩm kỹ thuật số của tập đoàn Sanofi, cho biết thêm:“ Trí tuệ nhân tạo đã được sử dụng từ lâu. Đúng là ChatGPT đã khiến người ta chú ý thêm đến cái được gọi là trí tuệ nhân tạo tạo sinh ( generative AI ), nhưng tập đoàn Sanofi đã sử dụng trí tuệ nhân tạo trong nhiều lĩnh vực. Nhiệm vụ của Sanofi là tìm ra phép màu của khoa học để cải thiện chất lượng sống của con người. Phép màu đó là những phân tử mới, những loại thuốc mới. Chúng tôi sử dụng large language model ( mô hình ngôn ngữ lớn ) trong trí tuệ nhân tạo tạo sinh để tìm ra những mục tiêu phân tử, chế ra những phân tử mới hiện chưa có. Con số các phân tử chưa được tìm ra nhiều hơn cả con số ngôi sao trong vũ trụ. Tiếp đến chúng tôi dùng trí tuệ nhân tạo để mô phỏng tác dụng của thuốc trên con người, trước khi thử nghiệm thật sự trên con ngườiViệc sử dụng trí tuệ nhân tạo trong chuỗi cung ứng của Sanofi đã chứng minh khả năng của công nghệ này dự đoán được 80%, giúp cho các ê kíp thi hành các biện pháp để bảo đảm tính liên tục của chuỗi cung ứng.Ngay cả trong việc thương mại hóa, trí tuệ nhân tạo được sử dụng để giúp các nhân viên quảng bá thuốc làm việc hiệu quả hơn, giúp các nhân viên y tế chữa trị bệnh nhân tốt hơn.”Trong việc ứng dụng trí tuệ nhân tạo vào nghiên cứu chế tạo, các tập đoàn dược phẩm như Sanofi cũng dựa vào những công ty khởi nghiệp, theo lời ông Emmanuel Frenehard: “Hãy tưởng tượng là ta tìm ra một phân tử mới. Chúng tôi làm việc với một công ty khởi nghiệp Pháp Mỹ mang tên Owkin. Đối với bốn loại ung thư, ta có thể thử nghiệm ảo để mô phỏng tác dụng của phân tử đó. Nếu phân tử chưa thật sự có hiệu quả, sự mô phỏng đó giúp chúng tôi nghiên cứu sâu hơn. Đó là một ví dụ cụ thể về việc ứng dụng trí tuệ nhân tạo.Đóng góp của Owkin là cung cấp cho các dữ liệu bệnh nhân ( dĩ nhiên đó là những bệnh nhân được giấu tên ), được thu thập từ các bệnh viện, từ các đối tác. Hai bên cùng đạt được những mô hình có thể cho kết quả tốt hoặc kết quả xấu, nhưng đó chỉ hoàn toàn là mô phỏng, để bảo đảm cho các thử nghiệm lâm sàng được thành công mỹ mãn. Tham vọng của chúng tôi là giảm phân nửa thời gian tìm ra một phân tử mới. Chúng tôi không chỉ mô phỏng tác dụng của một loại thuốc mới, mà còn dùng trí tuệ nhân tạo để tìm những mục tiêu phân tử mới, chẳng như có thể tìm ra một protein mới gần như là tức thì, trong khi một người phải mất rất nhiều ngày để tìm ra.Chúng tôi muốn trở thành tập đoàn sinh-dược đi xa nhất trong việc sử dụng trí tuệ nhân tạo. Điều quan trọng là phải sử dụng trí tuệ nhân tạo một cách có trách nhiệm, tức là một cách minh bạch, để chúng ta có thể tin tưởng vào nó, giúp tiết kiệm chi phí và giúp giảm bớt tác động đến môi trường. Sự bùng nổ của trí tuệ nhân tạo không làm chúng tôi bất ngờ, vì chúng tôi đã sử dụng công nghệ này từ lâu.”Như vậy phải chăng là sắp tới đây, trong các viện bào chế, sẽ không còn các nhân viên mặc áo blouse trắng cúi đầu vào kính hiển vi để chăm chú nghiên cứu những phân tử mới cho các loại thuốc mới, mà mọi thứ kể từ nay đều sẽ do trí tuệ nhân tạo đảm trách?Không phải như thế, bởi vì hiện vẫn còn nhiều khó khăn lớn trong việc sử dụng trí tuệ nhân tạo cho việc bào chế thuốc, thứ nhất là việc tiếp cận các dữ liệu có thể khai thác được. Tiếp đến là phải tìm cho ra các chuyên gia tương lai, vừa giỏi về trí tuệ nhân tạo, vừa có kiến thức chuyên môn về dược phẩm học. Mặt khác, như giải thích của Calum Chace, một chuyên gia về trí tuệ nhân tạo, các tập đoàn dược phẩm có quy mô rất lớn và mọi thay đổi quan trọng về cách thức nghiên cứu và phát triển sẽ ảnh hưởng đến rất nhiều người trong nhiều ban. Vị chuyên gia này nhấn mạnh: “ Rất khó mà thuyết phục tất cả những người đó chấp nhận một phương pháp làm việc hoàn toàn mới”Dầu sao thì máy móc sẽ không thể thay thế hoàn toàn nhân loại, nhất là trong một lĩnh vực hệ trọng đối với sức khỏe con người. Như giải thích của tiến sĩ Heba Sailem, chuyên gia về trí tuệ nhân tạo y sinh học, đại học King's College, Luân Đôn, tiềm năng của trí tuệ nhân tạo trong ngành công nghiệp dược phẩm là rất lớn, nhưng ngành này không nên vội vã, mà phải thi hành các biện pháp nghiêm ngặt trước khi dựa vào kết quả dự báo do trí tuệ nhân tạo đưa ra.

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Alexey Strygin is the Chief Digital Health Officer of Gero.ai and is leading Gerosense.ai, a digital biomarker for measuring biological age and enabling digital trials in humans at a drastically lower cost. Previously Alexey served as co-founder and CEO of Gray Matter. This company was developing therapeutic peptides solving age-related cognitive decline and longevity, CEO of Cryno Biotech, a company developing a novel GLP-1 agonist. Early team member of Insilico Medicine and Centaura. GERO is a biotechnology company using physics-based generative models on human health record datasets, combined with AI-guided drug design, to develop transformative interventions for aging and age-related diseases. To achieve this goal, GERO acquired a massive dataset of longitudinal, real-world medical records and built a generative, very large model of human health. The model is based on principles adopted from the physics of complex and dynamic systems and reveals the irreversible nature of human aging while highlighting the reversible aspects of diseases, and distinguishing the two phenomena. It also reveals causal relationships between microscopic molecular- and organism-level features associated with diseases to enable rapid targeting and drug discovery, directly in human data. GERO's preclinical-stage drug candidate demonstrates promising potential in addressing a diverse spectrum of neurodegenerative diseases. A separate ongoing drug discovery project to develop an anti-aging vaccine, instrumental in the treatment of senescence-associated diseases, has resulted in one of the most robust mice rejuvenation results globally. GERO's other earlier stage highly ambitious project aims to identify a drug aimed at significantly decelerating the aging process. The company's published research supports the notion that targeting the specific phenotype — the rate at which humans lose resilience — holds the key to achieving a substantial, several-fold extension of health span and life span, far exceeding so-called natural limits. https://sg.linkedin.com/in/strygin https://gerosense.ai https://gero.ai http://stryg.in https://twitter.com/strygah Watch our highest-viewed videos: 1-DR R VIJAYARAGHAVAN - PROF & PRINCIPAL INVESTIGATOR AT TIFR India's 1st Quantum Computer- https://youtu.be/ldKFbHb8nvQ 2-TATA MOTORS- DRIVING THE FUTURE OF MOBILITY IN INDIA- SHAILESH CHANDRA- MD: TATA MOTORS-https://youtu.be/M2Ey0fHmZJ0 3-MIT REPORT PREDICTS SOCIETAL COLLAPSE BY 2040 - GAYA HERRINGTON -DIR SUSTAINABILITY: KPMG- https://youtu.be/Jz29GOyVt04 4-WORLDS 1ST HUMAN HEAD TRANSPLANTATION- DR SERGIO CANAVERO - https://youtu.be/KY_rtubs6Lc 5-DR HAROLD KATCHER - CTO NUGENICS RESEARCH Breakthrough in Age Reversal- https://youtu.be/214jry8z3d4 6-Head of Artificial Intelligence-JIO - Shailesh Kumar https://youtu.be/q2yR14rkmZQ 7-STARTUP FROM INDIA AIMING FOR LEVEL 5 AUTONOMY - SANJEEV SHARMA CEO SWAAYATT ROBOTS - https://youtu.be/Wg7SqmIsSew 8-MAN BEHIND GOOGLE QUANTUM SUPREMACY - JOHN MARTINIS  - https://youtu.be/Y6ZaeNlVRsE 9-BANKING 4.0 - BRETT KING FUTURIST, BESTSELLING AUTHOR & FOUNDER MOVEN - https://youtu.be/2bxHAai0UG0 10-E-VTOL & HYPERLOOP- FUTURE OF INDIA" S MOBILITY- SATYANARAYANA CHAKRAVARTHY https://youtu.be/ZiK0EAelFYY 11-HOW NEUROMORPHIC COMPUTING WILL ACCELERATE ARTIFICIAL INTELLIGENCE - PROF SHUBHAM SAHAY- IIT KANPUR- https://youtu.be/sMjkG0jGCBs 12-INDIA'S QUANTUM COMPUTING INDUSTRY- PROF ARUN K PATI -DIRECTOR QETCI- https://youtu.be/Et98nkwiA8w Connect & Follow us at: https://in.linkedin.com/in/eddieavil https://in.linkedin.com/company/change-transform-india https://www.facebook.com/changetransformindia/ https://twitter.com/intothechange https://www.instagram.com/changetransformindia/ Don't Forget to Subscribe www.youtube.com/@toctwpodcast #ai #biotechnology #artificialintelligence

BUFFALO, NY- October 4, 2023 – A new research paper was published in Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 15, Issue 18, entitled, “Biomedical generative pre-trained based transformer language model for age-related disease target discovery.” Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. In their new study, researchers Diana Zagirova, Stefan Pushkov, Geoffrey Ho Duen Leung, Bonnie Hei Man Liu, Anatoly Urban, Denis Sidorenko, Aleksandr Kalashnikov, Ekaterina Kozlova, Vladimir Naumov, Frank W. Pun, Ivan V. Ozerov, Alex Aliper, and Alex Zhavoronkov from Insilico Medicine present a novel approach for predicting therapeutic targets using a large language model (LLM). “We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction.” This study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, the researchers retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, they propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in their approach. “Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.” DOI - https://doi.org/10.18632/aging.205055 Corresponding author - Alex Zhavoronkov - alex@insilico.com Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.205055 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, transformers, deep learning, therapeutic target discovery, aging biomarkers, human aging About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ X - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

This episode is sponsored by Shopify. Shopify is a commerce platform that allows anyone to set up an online store and sell their products. It's the leading commerce platform designed for a business of any size. Whether you're selling online, on social media, or in person, Shopify has you covered on every base. With Shopify you can sell physical and digital products. You can sell services, memberships, ticketed events, rentals and even classes and lessons. Sign up for a $1 per month trial period at shopify.com/eyeonai   On episode #134 of the Eye on AI podcast, Craig Smith sits down with  Alex Zhavoronkov, founder and CEO of Insilico Medicine. Being at the forefront of cutting-edge drug discovery and longevity, Alex leverages the power of AI to develop novel drugs that could potentially extend our lifespan.  In this episode we explore the unique tools Insilico Medicine uses to hypothesize protein targets and their links to diseases. Alex also gives us a peek into their automated robotic lab in Suzhou, China, which is expected to revolutionize drug development.  We delve deep into the world of drug discovery, with a special focus on Insilico Medicine's tools, Pandomics, and Chemistry42, that are reshaping the field. Yet beyond the science, we also discuss the broader implications of tackling aging - from overpopulation to the strain on healthcare and social security systems. Finally, we tackle the elephant in the room - the challenges in the pharmaceutical industry. Can AI expedite the drug discovery process? Alex certainly thinks so. We explore how AI can help identify partners quickly, streamline processes, and ultimately accelerate the development of drugs.  Join us as we unravel these mysteries and take you through the fascinating world of biotech, AI, and pharmaceutical research.   (0:00) Preview (01:36) Shopify (04:26) AI's Revolutionizing Drug Discovery (06:11) Inside AI Pharmaceutical Research (12:23) Use of AI tools in Medicine (19:56) Biotech Synergy: Collaborative Exploration and Target Discovery (37:17) Decoding Aging with AI (46:52) Aging, Overpopulation, and Drug Breakthroughs (1:07:33) Pharmaceutical Industry Challenges (1:20:42) How AI is Advancing Longevity through Medicine    Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

Discover how Insilico Medicine is accelerating drug discovery using Generative AI in the latest episode of the AWS Health Innovation Podcast. CEO Alex Zhavoronkov joins AWS' Dr. Yin He to discuss their journey and how they are accelerating drug development.

Microsoft-backed AI startup Inflection secures $1.3B funding, AI video startup Runway raises $141M, Overstock CEO discusses Bed Bath & Beyond rebranding, Costco addresses misuse of membership cards, United Airlines experiences highest flight cancellations, Insilico Medicine develops first AI-generated drug, Venus Et Fleur opens boutiques, Council Post highlights essential workforce skills, South Bay Credit Union partners with Greenlight Financial Technology, University of Kansas Cancer Center receives $100M lead gift.

Alex Zhavoronkov is our first guest to make a repeat appearance, having first joined us in episode 12, last November. We are delighted to welcome him back, because he is doing some of the most important work on the planet, and he has some important news.In 2014, Alex founded Insilico Medicine, a drug discovery company which uses artificial intelligence to identify novel targets and novel molecules for pharmaceutical companies. Insilico now has drugs designed with AI in human clinical trials, and it is one of a number of companies that are demonstrating that developing drugs with AI can cut the time and money involved in the process by as much as 90%. Selected follow-ups:https://insilico.com/ARDD 2023: https://agingpharma.org/Topics addressed in this episode include:*) For the first time, an AI-generated molecule has entered phase 2 human clinical trials; it's a  candidate treatment for IPF (idiopathic pulmonary fibrosis)*) The sequence of investigation: first biology (target identification), then chemistry (molecule selection), then medical trials; all three steps can be addressed via AI*) Pros and cons of going after existing well-known targets (proteins) for clinical intervention, versus novel targets*) Pros and cons of checking existing molecules for desired properties, versus imagining (generating) novel molecules with these properties*) Alex's experience with generative AI dates back to 2015 (initially with GANs - "generative adversarial networks")*) The use of interacting ensembles of different AI systems - different generators, and different predictors, allocating rewards*) The importance of "diversity" within biochemistry*) A way in which Insilico follows "the Apple model"*) What happens in Phase 2 human trials - and what Insilico did before reaching Phase 2*) IPF compared with fibrosis in other parts of the body, and a connection with aging*) Why probability of drug success is more important than raw computational speed or the cost of individual drug investigations*) Recent changes in the AI-assisted drug development industry: an investment boom in the wake of Covid, spiced-up narratives devoid of underlying substance, failures, downsizing, consolidation, and improved understanding by investors and by big pharma*) The AI apps created by Insilico can be accessed by companies or educational institutes*) Insilico research into quantum computing: this might transform drug discovery in as little as two years*) Real-world usage of quantum computers from IBM, Microsoft, and Google*) Success at Insilico depended on executive management task reallocation*) Can Longevity Escape Velocity be achieved purely by pharmacological interventions?*) Insilico's Precious1GPT approach to multimodal measurements of biological aging, and its ability to suggest new candidate targets for age-associated diseases: "one clock to rule them all"*) Reasons to mentally prepare to live to 120 or 150*) Hazards posed to longevity research by geopolitical tensions*) Reasons to attend ARDD in Copenhagen, 28 Aug to 1 Sept*) From longevity bunkers to the longevity dividendMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

A new research paper was published in Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 15, Issue 11, entitled, “Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery.” Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, researchers Anatoly Urban, Denis Sidorenko, Diana Zagirova, Ekaterina Kozlova, Aleksandr Kalashnikov, Stefan Pushkov, Vladimir Naumov, Viktoria Sarkisova, Geoffrey Ho Duen Leung, Hoi Wing Leung, Frank W. Pun, Ivan V. Ozerov, Alex Aliper, Feng Ren, and Alex Zhavoronkov from Insilico Medicine propose a novel approach to multimodal aging clock, which they call Precious1GPT, utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. “To identify aging biomarkers associated with age-related diseases, in the present work, we combined the ability of aging clocks to predict biological age and thus grasp molecular changes accompanied by senescence and our target ID approach to establish genes that are related to the development of diseases.” While the accuracy of the multimodal transformer is lower within each individual data type, compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately, it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, the researchers provided a list of promising targets annotated using the PandaOmics industrial target discovery platform. “The transformer-based model allowed for the integration of multi-omics data and improved the accuracy of the aging clock, while the transfer learning approach facilitated the identification of disease-related genes in the context of aging.” DOI - https://doi.org/10.18632/aging.204788 Corresponding author - Alex Zhavoronkov - alex@insilico.com Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.204788 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, transformers, deep learning, therapeutic target discovery, aging biomarkers, human aging About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

In this episode of Life Science Success my Guest is Alex Zhavoronkov.  Alex is the CEO Insilico Medicine an AI-powered drug discovery company. Insilico Medicine has progressed three internally-derived assets into human clinical trials. Insilico Medicine has discovered over 30 other programs in different stages. Insilico Medicine incorporates three engines in their platform: biology, chemistry, and clinical. They generate normal molecules with desired properties using 42 generative engines. They have a system that predicts clinical trials outcomes. Insilico Medicine's main strategy is partnering with pharma companies at the preclinical candidate stage. They value patient first, relentless innovation, and transparency and integrity. Alex's biggest concern is human tendency to prioritize unimportant things over important ones. Alex is excited about large language models, multimodal generative AI, robotics, and quantum computing.

In which Alex Zhavoronkov, founder and CEO of Insilico Medicine, discusses how AI can accelerate drug R&D, from target identification to clinical trials. He also shares his own path from computer science to biotech, explains how Insilico's business model has evolved, and gives advice on leading interdisciplinary teams.

Blog summary of a research paper published by Aging (Aging-US) in Volume 15, Issue 8: "Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics – an AI-enabled biological target discovery platform.” _____________________________________________________ Glioblastoma multiforme (GBM) is one of the most aggressive and fatal malignant brain tumors. With a median survival time of 15 months, only about 25% of patients survive for one year and less than 5% survive for five years. As people get older, the risk of developing GBM increases. The discovery of new drug targets for GBM is of paramount importance. The good news here is that high school students, Zachary Harpaz, Andrea Olsen and Christopher Ren, and researchers Anastasia Shneyderman, Alexander Veviorskiy, Maria Dralkina, Simon Konnov, Olga Shcheglova, Frank W. Pun, Geoffrey Ho Duen Leung, Hoi Wing Leung, Ivan V. Ozerov, Alex Aliper, Mikhail Korzinkin, and Alex Zhavoronkov have recently made remarkable strides in the joint field of aging and glioblastoma research. The team used a generative artificial intelligence (AI) engine from Insilico Medicine (founded by Dr. Alex Zhavoronkov) called PandaOmics, to identify new therapeutic targets for both GBM and aging. On April 26, 2023, their research paper was published in Aging's Volume 15, Issue 8, entitled, “Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics – an AI-enabled biological target discovery platform.” Full blog - https://aging-us.org/2023/05/high-school-students-use-ai-to-make-aging-and-glioblastoma-discoveries/ Research paper DOI - https://doi.org/10.18632/aging.204678 Corresponding author - Mikhail Korzinkin - mike@insilicomedicine.com Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.204678 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, target discovery, GBM, glioblastoma, PandaOmics About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

A new research paper was published in Aging (Aging-US) Volume 15, Issue 8, entitled, “Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform.” Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this new study, researchers Anastasia Shneyderman, Alexander Veviorskiy, Maria Dralkina, Simon Konnov, Olga Shcheglova, Frank W. Pun, Geoffrey Ho Duen Leung, Hoi Wing Leung, Ivan V. Ozerov, Alex Aliper, Mikhail Korzinkin, and Alex Zhavoronkov from The Youth Longevity Association, Pine Crest School Science Research Department, Shanghai High School International Division, and Insilico Medicine present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. “For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes.” Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, the researchers leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. They propose three potentially novel dual-purpose therapeutic targets to treat aging and GBM: cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1). “The next steps towards implementation of the identified therapeutic targets into the clinic would involve a generation of small molecules and their optimisation with further validation and preclinical testing to determine their safety, efficacy, and potential side effects.” DOI: https://doi.org/10.18632/aging.204678 Corresponding author - Mikhail Korzinkin - mike@insilicomedicine.com Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.204678 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, target discovery, GBM, glioblastoma, PandaOmics About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/@AgingJournal LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

From content writing, to providing human-like responses in chatbots, and to even coding. Generative AI has made its potential known to the world with the introduction of ChatGPT.  But besides, did you know that generative AI can be used in the process of drug discovery as well?  Today we speak with Insilico Medicine, a clinical stage AI driven drug discovery company that is delivering breakthrough solutions to discover and develop medicines in areas such as cancer, immunity and ageing. The company was selected by NVIDIA as one of the top 5 AI companies for its potential for social impact back in 2017. But how does it work, and what does a new drug discovery mean for the company? Separately, Insilico Medicine had also signed a strategic research collaboration with pharmaceutical giant Sanofi in a deal worth up to US$1.2 billion last November. So, what is the status right now? On Under the Radar, Drive Time's finance presenter Chua Tian Tian posed these questions to Dr Alex Zhavoronkov, CEO of Insilico Medicine. See omnystudio.com/listener for privacy information.

#ai #generativeai #drugdiscovery #pharma In this episode of CXOTalk, we have the pleasure of speaking with Dr. Alex Zhavoronkov, the founder and CEO of Insilico Medicine.Insilico Medicine uses artificial intelligence to enhance drug discovery. By combining generative adversarial networks (GANs), reinforcement learning, and other AI techniques, Insilico streamlines the design, synthesis, and testing of new molecules. Their approach has garnered attention, raising $400 million in funding so far.Dr. Zhavoronkov shares insights into Insilico's goals, such as the accelerated development and testing of small molecules targeting specific diseases. We also explore how their software impacts pharmaceutical R&D by enabling researchers to investigate new targets, design molecules with certain properties, and potentially predict the outcomes of clinical trials.Join us as we discuss the evolving landscape of pharmaceuticals and how generative AI can help discover new treatments for chronic diseases and promote a healthier future.The conversion covers these topics:► Early generative AI experiments & adversarial networks► Generative AI in molecular drug design► Advancements: AI techniques & reinforcement learning► Insilico Medicine's funding journey & challenges► Unique challenges in AI-based drug discovery► First validation of AI-generated molecules► Software for chemistry & biology applications► Traditional vs. Insilico Medicine's approach► Pharma challenges: high costs, low novelty, and diminishing returns► Potential billion-dollar payout for successful Phase II drugs► AI in drug development can increase success probability► Early partnerships with large pharma and lessons learned► Decision to stop doing pilots with big pharma companies► Generative AI and public data► De-biasing pharmaceutical research► Automating the workflow and quality control► Reinforcing generative AI with real experiments► “Drug discovery is brutal”► Drug discovery democratization► AI in medical writing► IP risks and generative AI► AI and robotics to prevent agingVisit our website for the audio podcast: https://www.cxotalk.com/episode/future-of-drug-discovery-generative-ai-in-pharma-and-medicineSubscribe to the newsletter: https://www.cxotalk.com/subscribeCheck out our upcoming live shows: https://www.cxotalk.comAlex Zhavoronkov, Ph.D. is the founder and CEO of Insilico Medicine, a leader in next-generation artificial intelligence technologies for drug discovery and biomarker development. He is also the founder of Deep Longevity, Inc, a spin-off of Insilico Medicine developing a broad range of artificial intelligence-based biomarkers of aging and longevity servicing healthcare providers and life insurance industry. In 2020, Deep Longevity was acquired by Endurance Longevity (HK: 0575). Beginning in 2015, he invented critical technologies in the field of generative adversarial networks (GANs) and reinforcement learning (RL) for the generation of novel molecular structures with the desired properties and generation of synthetic biological and patient data. He also pioneered applications of deep learning technologies for the prediction of human biological age using multiple data types, and transferred learning from aging into disease, target identification, and signaling pathway modeling. Under his leadership, Insilico has raised over $400 million in multiple rounds from expert investors, opened R&D centers in six countries or regions, and partnered with multiple pharmaceutical, biotechnology, and academic institutions, nominated 11 preclinical candidates, and has generated positive topline Phase 1 data in human clinical trials with an AI-discovered novel target and AI-designed novel molecule for idiopathic pulmonary fibrosis that received Orphan Drug Designation from the FDA and is nearing Phase 2 clinical trials. Insilico also recently announced that its generative AI-designed drug for COVID-19 and related variants was approved for clinical trials.Prior to founding Insilico, he worked in senior roles at ATI Technologies (a GPU company acquired by AMD in 2006), NeuroGNeuroinformatics, and the Biogerontology Research Foundation. Since 2012, he has published over 150 peer-reviewed research papers, and 2 books including "The Ageless Generation: How Biomedical Advances Will Transform the Global Economy" (Macmillan, 2013). He serves on the advisory or editorial boards of Trends in Molecular Medicine, Aging Research Reviews, Aging, Frontiers in Genetics, and founded and co-chairs the Annual Aging Research and Drug Discovery conference, the world's largest event on aging in the pharmaceutical industry. He is an adjunct professor of artificial intelligence at the Buck Institute for Research on Aging.

It may feel like generative AI technology suddenly burst onto the scene over the last year or two, with the appearance of text-to-image models like Dall-E and Stable Diffusion, or chatbots like ChatGPT that can churn out astonishingly convincing text thanks to the power of large language models. But in fact, the real work on generative AI has been happening in the background, in small increments, for many years. One demonstration of that comes from Insilico Medicine, where Harry's guest this week, Alex Zhavoronkov, is the co-CEO. Since at least 2016, Zhavoronkov has been publishing papers about the power of a class of AI algorithms called generative adversarial networks or GANs to help with drug discovery. One of the main selling points for GANs in pharma research is that they can generate lots of possible designs for molecules that could carry out specified functions in the body, such as binding to a defective protein to stop it from working. Drug hunters still have to sort through all the possible molecules identified by GANs to see which ones will actually work in vitro or in vivo, but at least their pool of starting points can be bigger and possibly more specific.Zhavoronkov says that when Insilico first started touting this approach back in the mid-2010s, few people in the drug business believed it would work. So to persuade investors and partners of the technology's power, the company decided to take a drug designed by its own algorithms all the way to clinical trials. And it's now done that. This February the FDA granted orphan drug designation to a small-molecule drug Insilico is testing as a treatment for a form of lung scarring called idiopathic pulmonary fibrosis. Both the target for the compound, and the design of the molecule itself, were generated by Insilico's AI. The designation was a big milestone for the company and for the overall idea of using generative models in drug discovery. In this week's interview, Zhavoronkov talks about how Insilico got to this point; why he thinks the company will survive the shakeout happening in the biotech industry right now; and how its suite of generative algorithms and other technologies such as robotic wet labs could change the way the pharmaceutical industry operates.For a full transcript of this episode, please visit our episode page at http://www.glorikian.com/podcast Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.

Are you ready to dive into the future of medicine and the revolutionary advancements being made with artificial intelligence? In this episode, I sit down with Alex Zhavoronkov, the founder and CEO of Insilico Medicine, a global company focused on the discovery of novel therapeutics utilizing cutting-edge technology in healthcare.From longevity research to anti-aging treatments, Alex shares his expertise on themost impactful field of our time, discussing his work in protein targetdiscovery and the design of small molecule drugs using generative AI.But it doesn't stop there. Alex also shares insights into the development of agingclocks, biomarkers of aging, and precision medicine. Discover how generative AIis transforming the pharmaceutical industry, improving clinical trials, andproviding a revolutionary new approach to drug discovery methods.Don't miss out on this incredible opportunity to learn from one of the leadingexperts in AI and healthcare innovation. Tune in now and join us on thisfascinating journey into the future of medicine.

For episode 7, we chat with Alex Zhavoronkov, Founder & CEO at Insilico Medicine.Stay tuned and find out which partnerships are crucial for very early stages in learning and gaining experience in drug discovery. First In Human is a biotech-focused podcast that interviews industry leaders and investors to learn about their journey to in-human clinical trials. Presented by Vial, a tech-enabled CRO, hosted by Simon Burns, CEO & Co-Founder. Episodes launch weekly on Tuesdays. To view the full episode transcript, click here.  Interested in being featured as a guest on First In Human? Please reach out to catie@vial.com.

Petrina Kamya, head of AI platforms at Insilico Medicine, talks to Andrew Brosnan, principal analyst in AI and Applied Intelligence at Omdia, to take a deep dive into the intricacies of AI-powered drug discovery.

This episode discusses progress at Insilico Medicine, the AI drug development company founded by our guest, longevity pioneer Alex Zhavoronkov.1.20 In Feb 2022, Insilico got an IPF drug into phase 1 clinical trials: a first for a wholly AI-developed drug1.50  Insilico is now well-funded; its software is widely used in the pharma industry2.30 How drug development works. First you create a hypothesis about what causes a disease4.00 Pandaomics is Insilico's software to generate hypotheses. It combines 20+ AI models, and huge public data repositories6.00 This first phase is usually done in academia. It usually costs $ billions to develop a hypothesis. 95% of them fail6.50 The second phase is developing a molecule which might treat the disease7.15 This is the job of Insilico's Chemistry 42 platform7.30 The classical approach is to test thousands of molecules to see if they bind to the target protein7.50 AI, by contrast, is able to "imagine" a novel molecule which might bind to it8.00 You then test 10-15 molecules which have the desired characteristics8.20 This is done with a variety of genetic algorithms, Generative Adversarial Networks (GANs), and some Transformer networks8.35 Insilico has a “zoo” of 40 validated models10.40 Given the ten-fold improvement, why hasn't the whole drug industry adopted this process?10.50 They do all have AI groups and they are trying to change, but they are huge companies, and it takes time11.50 Is it better to invent new molecules, or re-purpose old drugs, which are already known to be safe in humans?13.00 You can't gain IP with re-purposed drugs: either somebody else “owns” them, or they are already generic15.00 The IPF drug was identified during aging research, using aging clocks, and a deep neural net trained on longitudinal data17.10 The third phase is where Insilico's other platform, InClinico, comes into play17.35 InClinico predicts the results of phase 2 (clinical efficacy) trials18.15 InClinico is trained on massive data sets about previous trials19.40 InClinico is actually Insilico's oldest system. Its value has only been ascertained now that some drugs have made it all the way through the pipeline22.05 A major pharma company asked Insilico to predict the outcome of ten of its trials22.30 Nine of these ten trials were predicted correctly23.00 But the company decided that adopting this methodology would be too much of an upheaval; it was unwilling to rely on outsiders so heavily24.15 Hedge funds and banks have no such qualms24.25 Insilico is doing pilots for their investments in biotech startups26.30 Alex is from Latvia originally, studied in Canada, started his career in the US, but Insilico was established in Hong Kong. Why?27.00 Chinese CROs, Contract Research Organisations, enable you to do research without having your own wetlab 28.00 Like Apple, Insilico designs in the US and does operations in China. You can also do clinical studies there28.45 They needed their own people inside those CROs, so had to be co-located29.10 Hong Kong still has great IP protection, financial expertise, scientific resources, and is a beautiful place to live29.40 Post-Covid, Insilico also had to set up a site in Shanghai30.35 It is very frustrating how much opposition has built up against international co-operation32.00 Anti-globalisation ideas and attitudes are bad for longevity research, and all of biotech33.20 Insilico has all the data it needs. Its bottleneck is talent35.00 Another requirement is co-operation from governments and regulators, who often struggle to sort the chaff from the wheat in self-proclaimed AI companies37.00 Longevity research is the most philanthropic activity in the world37.30 Longevity Medicine Course is available to get clinical practitioners up to speed with the sector

A new research paper was published on the cover of Aging (listed as “Aging (Albany NY)” by Medline/PubMed and “Aging-US” by Web of Science) Volume 14, Issue 18, entitled, “Psychological factors substantially contribute to biological aging: evidence from the aging rate in Chinese older adults.” Aging clocks are statistical models that enable measurements of biological age, as opposed to chronological age. While the latter is determined by one's date of birth, the former depends on the intensity of aging processes and can be affected by genetics, life choices, and the environment. Most commonly, such aging clocks are regressors, trained to predict a person's chronological age based on a vector of input parameters, such as clinical blood test results, gene expression levels, or DNA methylation intensities. In a new study, researchers Fedor Galkin, Kirill Kochetov, Diana Koldasbayeva, Manuel Faria, Helene H. Fung, Amber X. Chen, and Alex Zhavoronkov from Deep Longevity, Stanford University, The Chinese University of Hong Kong, Insilico Medicine, and the Buck Institute for Research on Aging developed a deep learning aging clock using blood test data from the China Health and Retirement Longitudinal Study (CHARLS), which has a mean absolute error of 5.68 years. “Using data from the Chinese CHARLS database, we have demonstrated that organismal aging is not only determined by physical factors but also, to a certain degree, affected by mental state and social status.” The clock detects accelerated aging in people with heart, liver, and lung conditions. The researchers demonstrated that psychological factors, such as feeling unhappy or being lonely, add up to 1.65 years to one's biological age, and the aggregate effect exceeds the effects of biological sex, living area, marital status, and smoking status. They concluded that the psychological component should not be ignored in aging studies due to its significant impact on biological age. The study findings further support the necessity of companionship and a psychologically pleasant environment for healthy longevity. “We interpreted biological age as a proxy for the general state of health and show that positive feelings (happiness, hope, safety) have a significant impact on the former.” DOI: https://doi.org/10.18632/aging.204264 Corresponding Author: Fedor Galkin – Email: fedor@deeplongevity.com Keywords: psychological aging, lifespan psychology, aging clocks, longevity Sign up for free Altmetric alerts about this article: https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.204264 About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/agingus​ LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

Joining Chris today is Alex Zhavoronkov, CEO and Co-founder of Insilico Medicine, an artificial intelligence–driven pharma-technology company with a mission to accelerate drug discovery and development. Alex is a lifelong advocate for longevity biotech and the author of The Ageless Generation: How Advances in Biomedicine Will Transform the Global Economy. Today, Alex shares the accomplishments that Insilico Medicine has achieved in drug discovery and how AI and robotics come into play. The episode begins with Alex narrating his experience in the field of longevity and how his interest developed at a young age. He discusses the reason behind building Insilico Medicine, how AI and robotics aid drug discovery in the longevity industry, and how biology and chemistry play a significant role at Insilico Medicine. The episode ends with Alex describing the future he sees for Insilico Medicine and how they can improve human life using AI to advance drug discovery and data generation. Episode Highlights: What fueled Alex's interest in longevity Building Insilico Medicine How tech is used at Insilico Medicine The role of AI and robotic systems How AI and robotic systems can improve the longevity space The future of Insilico Medicine Quotes: “It always fascinated me how we grow, mature, reach our peak, and then decline and die. At the end of the day, it doesn't matter what you do, you lose everything… So, the rest of my life is dedicated to aging research.” “We started generating novel molecular structures with the desired properties, and managed to achieve spectacular results.” “In human clinical trials, we realized that we can use some incremental data that could be generated using a robotic system. So now we're building one of the most advanced labs in the world focused on data generation, and also personalized medicine that can take in specific biological samples.” “BioAge is one of the leaders in the space, showcasing that it can identify targets using longitudinal data that is available from biobanks.” “We trained neural networks to predict age first, and then retrain them on diseases or on other conditions, that is, any data type that is changing in time.” “By training on age, you are training on the most important feature that connects all of us.” Links: Email questions, comments, and feedback to podcast@bioagelabs.com Translating Aging on Twitter:https://twitter.com/BioAgePodcast ( @bioagepodcast) BIOAGE Labs Websitehttps://bioagelabs.com/ ( BIOAGELabs.com) BIOAGE Labs Twitterhttps://twitter.com/bioagelabs?lang=en ( @bioagelabs) BIOAGE Labshttps://www.linkedin.com/company/bioage-labs/ ( LinkedIn) https://insilico.com/ (Insilico Medicine)

Listen to a blog summary of a research paper chosen as the cover for Volume 14, Issue 6 of Aging (Aging-US), entitled, "Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine." _________________________________ What if drugs designed to treat conditions such as diabetes, osteoporosis and rheumatoid arthritis could at the same time provide patients with anti-aging benefits? On March 29, 2022, researchers—from Insilico Medicine, University of Chicago, George Mason University, University of Liverpool, and Buck Institute for Research on Aging—released a new study on the cover of Aging (Aging-US) Volume 14, Issue 6, about Insilico's next-generation artificial intelligence (AI)-powered discovery software, called the PandaOmics platform. Their trending research paper is entitled, “Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine.” Full blog - https://aging-us.org/2022/03/pandaomics-identifies-duel-targets-of-aging-and-age-related-diseases/ DOI - https://doi.org/10.18632/aging.203960 Corresponding author - Alex Zhavoronkov - alex@insilico.com Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.20396 Keywords - aging, artificial intelligence, deep learning, drug discovery, multi-omics, target identification About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at http://www.Aging-US.com​​ or connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/agingus​ LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Aging-US is published by Impact Journals, LLC: http://www.ImpactJournals.com​​ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

Aging Editorial Board member and Founder and CEO of Insilico Medicine, Dr. Alex Zhavoronkov, discusses his 2020 COVID-19 research perspective published by Aging (Aging-US), entitled, "Geroprotective and senoremediative strategies to reduce the comorbidity, infection rates, severity, and lethality in gerophilic and gerolavic infections." DOI - https://doi.org/10.18632/aging.102988 Corresponding author - Alex Zhavoronkov - alex@insilico.com Abstract: The recently identified SARS-CoV-2 betacoronavirus responsible for the COVID-19 pandemic has uncovered the age-associated vulnerability in the burden of disease and put aging research in the spotlight. The limited data available indicates that COVID-19 should be referred to as a gerolavic (from Greek, géros “old man” and epilavís, “harmful”) infection because the infection rates, severity, and lethality are substantially higher in the population aged 60 and older. This is primarily due to comorbidity but may be partially due to immunosenescence, decreased immune function in the elderly, and general loss of function, fitness, and increased frailty associated with aging. Immunosenescence is a major factor affecting vaccination response, as well as the severity and lethality of infectious diseases. While vaccination reduces infection rates, and therapeutic interventions reduce the severity and lethality of infections, these interventions have limitations. Previous studies showed that postulated geroprotectors, such as sirolimus (rapamycin) and its close derivative rapalog everolimus (RAD001), decreased infection rates in a small sample of elderly patients. This article presents a review of the limited literature available on geroprotective and senoremediative interventions that may be investigated to decrease the disease burden of gerolavic infections. This article also highlights a need for rigorous clinical validation of deep aging clocks as surrogate markers of biological age. These could be used to assess the need for, and efficacy of, geroprotective and senoremediative interventions and provide better protection for elderly populations from gerolavic infections. This article does not represent medical advice and the medications described are not yet licensed or recommended as immune system boosters, as they have not undergone clinical evaluation for this purpose. Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.102988 Press release - https://www.aging-us.com/news_room/scientist-proposes-clinical-trials-w-low-dose-rapamycin-to-protect-elderly-from-covid-19 Keywords - COVID-19, SARS-CoV-2, coronavirus, sirolimus, rapalog About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at http://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/agingus​ LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ Aging-US is published by Impact Journals, LLC: http://www.ImpactJournals.com​​ Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

Alexey Strygin is a longevity enthusiast and bio-entrepreneur, co-founder of Gray Matter (part of Lactocore Group), a company focused on the development of therapeutic peptides vs. mild cognitive impairment, other age-related CNS diseases, and aging. Alexey teaches a course on Entrepreneurship in Biotechnology in Masters Program at Moscow State University. Alexey is a fellow in 1st cohort of On Deck Longevity Biotech or ODLB. It is a continuous community for people to come together to build, join, or invest in revolutionary longevity biotechnology startups. Previous experience includes Insilico Medicine, a unicorn pioneering AI application for drug discovery, Centaura focused on radical interventions in aging, Cryno, and Skolkovo Foundation. FIND ALEXEY ON SOCIAL MEDIA LinkedIn | Facebook | Instagram | Twitter ================================ SUPPORT & CONNECT: Support on Patreon: https://www.patreon.com/denofrich Twitter: https://twitter.com/denofrich Facebook: https://www.facebook.com/denofrich YouTube: https://www.youtube.com/denofrich Instagram: https://www.instagram.com/den_of_rich/ Hashtag: #denofrich © Copyright 2022 Den of Rich. All rights reserved.

Alexey Strygin is a longevity enthusiast and bioentrepreneur, co-founder of Gray Matter (part of Lactocore Group), a company focused on the development of therapeutic peptides vs. mild cognitive impairment, other age-related CNS diseases, and aging. Alexey teaches a course on Entrepreneurship in Biotechnology in Masters Program at Moscow State University. Alexey is a fellow in 1st cohort of On Deck Longevity Biotech or ODLB. It is a continuous community for people to come together to build, join, or invest in revolutionary longevity biotechnology startups. Previous experience includes Insilico Medicine, a unicorn pioneering AI application for drug discovery, Centaura focused on radical interventions in aging, Cryno, and Skolkovo Foundation.FIND ALEXEY ON SOCIAL MEDIALinkedIn | Facebook | Instagram | Twitter================================PODCAST INFO:Podcast website: https://www.uhnwidata.com/podcastApple podcast: https://apple.co/3kqOA7QSpotify: https://spoti.fi/2UOtE1AGoogle podcast: https://bit.ly/3jmA7ulSUPPORT & CONNECT:Support on Patreon: https://www.patreon.com/denofrichTwitter: https://www.instagram.com/denofrich/Instagram: https://www.instagram.com/denofrich/Facebook: https://www.facebook.com/denofrich

AI research wunderkind, DeepMind, has long been all fun and games. The London-based organization, owned by Google parent company Alphabet, has used deep learning to train algorithms that can take down world champions at the ancient game of Go and top players of the popular strategy video game Starcraft. Then last year, things got serious when DeepMind trounced the competition at a protein folding contest. Predicting the structure of proteins, the complex molecules underpinning all biology, is notoriously difficult. But DeepMind's AlphaFold2 made a quantum leap in capability, producing results that matched experimental data down to a resolution of a few atoms. In July, the company published a paper describing AlphaFold2, open-sourced the code, and dropped a library of 350,000 protein structures with a promise to add 100 million more. This week, Alphabet announced it will build on DeepMind's AlphaFold2 breakthrough by creating a new company, Isomorphic Labs, in an effort to apply AI to drug discovery. “We are at an exciting moment in history now where these techniques and methods are becoming powerful and sophisticated enough to be applied to real-world problems including scientific discovery itself,” wrote Demis Hassabis, DeepMind founder and CEO, in a post announcing the company. “Now the time is right to push this forward at pace, and with the dedicated focus and resources that Isomorphic Labs will bring.” Hassabis is Isomorphic's founder and will serve as its CEO while the fledgling company gets its feet, setting the agenda and culture, building a team, and connecting the effort to DeepMind. The two companies will collaborate, but be largely independent. “You can think of [Isomorphic] as a sort of sister company to DeepMind,” Hassabis told Stat. “The idea is to really forge ahead with the potential for computational AI methods to reimagine the whole drug discovery process.” While AlphaFold2's success sparked the effort, protein folding is only one step—arguably simpler than others—in the arduous drug discovery process. Hassabis is thinking bigger. Though details are scarce, it appears the new company will build a line of AI models to ease key choke points in the process. Instead of identifying and developing drugs themselves, they'll sell a platform of models as a service to pharmaceutical companies. Hassabis told Stat these might tackle how proteins interact, the design of small molecules, how well molecules bind, and the prediction of toxicity. That the work will be separated from DeepMind itself is interesting. The company's not insignificant costs have largely been dedicated to pure research. DeepMind turned its first profit in 2020, but its customers are mostly Alphabet companies. Some have wondered if it'd face more pressure to focus on commercial products. The decision to create a separate enterprise based on DeepMind research seems to indicate that's not yet the case. If it can keep pushing the field ahead as a whole, perhaps it makes sense to fund a new organization—or organizations, seeded by future breakthroughs—as opposed to diverting resources from DeepMind's more foundational research. Isomorphic Labs has plenty of company in its drug discovery efforts. In 2020, AI in cancer, molecular, and drug discovery received the most private investment in the field, attracting over $13.8 billion, more than quadruple 2019's total. There have been three AI drug discovery IPOs in the last year, and mature startups—including Exscientia, Insilico Medicine, Insitro, Atomwise, and Valo Health—have earned hundreds of millions in funding. Companies like Genentech, Pfizer, and Merck are likewise working to embed AI in their processes. To a degree, Isomorphic will be building its business from the ground up. AlphaFold2 is without a doubt a big deal, but protein modeling is the tip of the drug discovery iceberg. Also, while AlphaFold2 had the benefit of access to hundreds of thousands of freely available, already modeled protein s...

Dr. Evelyne Bischof, MD  is an expert in internal medicine and oncology, with a focus on preventative and precision medicine, bio-gerontology, and geronto-oncology. Dr. Bischof is deeply passionate about next-generation medical technology, and the applications of artificial intelligence for biomedical research and practice. Dr. Bischof spent a decade practicing medicine and performing translational research in Switzerland, US, and China. Dr. Bischof is a medical doctor with an MD from Max Planck Institute for Molecular Biology and Genetics, and interned at Columbia University, Harvard MGH, and Beth Israel Medical Deaconess. Dr. Bischof is the author of over 40 peer-reviewed papers and is a frequent speaker at scientific and medical conferences. Dr. Bischof serves as Assistant Professor - Shanghai University of Medicine and Health Sciences; Associate Faculty Shanghai Jiao Tong University, and Researcher at University Hospital of Basel. In addition to those roles, Dr. Bischof is Longevity Physician at Human Longevity, Inc., Clinical Advisor, Insilico Medicine, and Scientific Advisor, Holmusk. Dr. Bischof is also Chair of the Visionary Board of The Longevity Science Foundation (https://longevity.foundation/), a new Swiss foundation committed to distributing more than $1 billion over the next ten years to research, institutions and projects advancing healthy human longevity and extending the healthy human lifespan to more than 120 years.

“Humans are the only species that understand that they are aging and dying. So we try to avoid thinking about aging and we are now starting to think about prevention.”Alex Zhavoronkov, Ph.D., is the founder and Chief Longevity Officer of Deep Longevity, Inc, a global company developing a broad range of artificial intelligence-based biomarkers of aging and longevity. He is also the CEO and Founder of Insilico Medicine.In this episode, he explains the approach Deep Longevity and Young.ai are taking, the various clocks and machine learning methods they are using, the necessity for physician education, and coordinated building of a longevity ecosystem spanning physicians, clinics, insurers, academia, pharma, and more. He also goes into detail about a promising new interesting field of longevity psychology and connected subjective and psychological clocks.Music:Is That You or Are You You by Chris Zabriskie is licensed under a Creative Commons Attribution 4.0 license. https://creativecommons.org/licenses/by/4.0/Session summary: Longevity as a Service: AI & Aging Clocks | Alex Zhavoronkov, Deep Longevity & Insilico Medicine - Foresight InstituteThe Foresight Institute is a research organization and non-profit that supports the beneficial development of high-impact technologies. Since our founding in 1987 on a vision of guiding powerful technologies, we have continued to evolve into a many-armed organization that focuses on several fields of science and technology that are too ambitious for legacy institutions to support.Allison Duettmann is the president and CEO of Foresight Institute. She directs the Intelligent Cooperation, Molecular Machines, Biotech & Health Extension, Neurotech, and Space Programs, Fellowships, Prizes, and Tech Trees, and shares this work with the public. She founded Existentialhope.com, co-edited Superintelligence: Coordination & Strategy, co-authored Gaming the Future, and co-initiated The Longevity Prize. Apply to Foresight's virtual salons and in person workshops here!We are entirely funded by your donations. If you enjoy what we do please consider donating through our donation page.Visit our website for more content, or join us here:TwitterFacebookLinkedInEvery word ever spoken on this podcast is now AI-searchable using Fathom.fm, a search engine for podcasts.  Hosted on Acast. See acast.com/privacy for more information.

David Silver, head of the Reinforcement Learning research group at DeepMind, is awarded the honorary ranking of "Ninth Dan" for AlphaGo. https://www.cnbc.com/2021/06/18/computer-scientists-ask-if-deepmind-can-ever-make-ai-human-like.html Insilico Medicine, an AI-based drug development and discovery platform, announced Tuesday Series C funding of $ 255 million. https://techcrunch.com/2021/06/22/a-i-drug-discovery-platform-insilico-medicine-announces-255-million-in-series-c-funding/?guccounter=1 XPRIZE, the world's leading provider of incentive competitions to solve the world's great challenges, and IBM Watson, IBM's AI technology for business, today announced the grand prize winner of the $ 5 million IBM Watson AI XPRIZE Challenge. " https://www.businesswire.com/news/home/20210623005848/en/Grand-Prize-Winner-Announced-in-5M-IBM-Watson-AI-XPRIZE-Competition NVIDIA announced Canvas, an app available in free beta that provides real-time painting tools for anyone with an NVIDIA RTX GPU. https://petapixel.com/2021/06/23/nvidia-new-canvas-app-uses-ai-to-turn-doodles-into-realistic-photos/ China's first female virtual student, developed by Tsinghua University, met with fans Thursday when she opened an account on China's Twitter-like Sina Weibo platform. https://www.globaltimes.cn/page/202106/1225392.shtml Visit www.integratedaisolutions.com

Podcast jest dostępny także w formie newslettera: https://ainewsletter.integratedaisolutions.com/ David Silver, lider grupy badawczej ds. https://www.cnbc.com/2021/06/18/computer-scientists-ask-if-deepmind-can-ever-make-ai-human-like.html Insilico Medicine, oparta na sztucznej inteligencji platforma do opracowywania i odkrywania leków, ogłosiła we wtorek 255 milionów dolarów finansowania serii C. https://techcrunch.com/2021/06/22/a-i-drug-discovery-platform-insilico-medicine-announces-255-million-in-series-c-funding/?guccounter=1 XPRIZE, światowy lider w projektowaniu i prowadzeniu konkursów motywacyjnych mających na celu rozwiązywanie wielkich wyzwań ludzkości, oraz IBM Watson, technologia AI IBM dla biznesu, https://www.businesswire.com/news/home/20210623005848/en/Grand-Prize-Winner-Announced-in-5M-IBM-Watson-AI-XPRIZE-Competition NVIDIA ogłosiła Canvas, aplikację dostępną jako bezpłatną wersję beta, która, jak twierdzi, zapewnia narzędzia do malowania w czasie rzeczywistym każdemu, kto ma procesor graficzny NVIDIA RTX. https://petapixel.com/2021/06/23/nvidia-new-canvas-app-uses-ai-to-turn-doodles-into-realistic-photos/ Pierwsza chińska wirtualna studentka opracowana przez Uniwersytet Tsinghua spotkała się z fanami w czwartek, kiedy otworzyła konto na chińskiej platformie Sina Weibo podobnej do Twittera. https://www.globaltimes.cn/page/202106/1225392.shtml Odwiedź www.integratedaisolutions.com

David Silver, Leiter der Forschungsgruppe Reinforcement Learning bei DeepMind, wird für AlphaGo mit dem Ehren-Rangliste "Neunter Dan" ausgezeichnet. https://www.cnbc.com/2021/06/18/computer-scientists-ask-if-deepmind-can-ever-make-ai-human-like.html Insilico Medicine, eine KI-basierte Plattform für die Entwicklung und Entdeckung von Medikamenten, kündigte am Dienstag eine Serie-C-Finanzierung in Höhe von 255 Millionen US-Dollar an. https://techcrunch.com/2021/06/22/a-i-drug-discovery-platform-insilico-medicine-announces-255-million-in-series-c-funding/?guccounter=1 XPRIZE, der weltweit führende Anbieter von Incentive-Wettbewerben zur Lösung der großen Herausforderungen der Menschheit, und IBM Watson, IBMs KI-Technologie für Unternehmen https://www.businesswire.com/new s/home/20210623005848/en/Grand-Prize-Winner-Announced-in-5M-IBM-Watson-AI-XPRIZE-Competition NVIDIA hat Canvas angekündigt, eine App, die als kostenlose Betaversion verfügbar ist und die Echtzeit-Malwerkzeuge für jeden mit einer NVIDIA RTX-GPU bietet. https://petapixel.com/2021/06/23/nvidia-new-canvas-app-uses-ai-to-turn-doodles-into-realistic-photos/ Chinas erste virtuelle Studentin, die von der Tsinghua-Universität entwickelt wurde, traf sich am Donnerstag mit Fans, als sie ein Konto auf Chinas Twitter-ähnlicher Sina Weibo-Plattform eröffnete. https://www.globaltimes.cn/page/202106/1225392.shtml Visit www.integratedaisolutions.com

Boston's Transmit Security, which provides workforces risk management and passwordless identity solutions, has announced its Series A fundraise of $543 million, led by Insight Partners and General Atlantic. Additional investors include Cyberstarts, Geodesic, SYN Ventures, Vintage, and Artisanal Ventures. The company's pre-investment valuation was $2.2B, and it looks to work towards passwordless protection and identity risk management.Oyster, a software HR platform to address hiring problems in companies, announced its Series B fundraise of $50M, led by Stripes. The funding comes on the heels of its Series A funding, which it raised in February this year from San Mateo-based Emergence Capital. Chicago-based G2, a marketplace for business organizations to buy, research and manage their software, has attained unicorn status, raising $157M in a Series D funding round led by Permira and bags the unicorn tag. The round has valued the startup at $1.1 Billion.Insilico Medicine, an AI-based platform for medication discovery and research, has raised $255 million in a Series C round. It is a Hong Kong-based company founded in 2014 to discover novel drug targets for currently untreatable illnesses. In 2020, the firm identified a novel therapeutic target for idiopathic pulmonary fibrosis (IPF), a disease in which the tiny air sacs in the lungs scar and make breathing difficult.Splunk, a company that develops operational intelligence software, has secured $1 billion in funding from private equity firm Silver Lake. Silverlake's chair and managing partner, Ken Hao, will join the Splunk board of directors. The San Francisco-based company has introduced Splunk Security Cloud. Splunk also announced the completion of its acquisition of TruStar, a developer of security software.Mollie, a unicorn payments service provider in Europe, has announced its Series C fundraise of €665 million ($800 million) in a round led by Blackstone's growth equity investing business- Blackstone Growth (BXG). The company revealed that its valuation has touched €5.5 billion ($6.5 billion) post this round, making it one of the top five most valued privately held fintech companies in Europe.Merlyn Mind, an AI assistant for teachers, has received $29 million as initial funding from edtech investor Learn Capital and other undisclosed investors. With this announcement, Merlyn Mind has come out of stealth. It spent nearly three years developing its first version and, more recently, piloting the service in more than 50 classes across more than 20 schools.Zeller, a fintech company based in Melbourne that serves SMBs, has announced that it has raised AUD 50 million at an AUD 400 million valuation. The round was led by Spark Capital. Other participants include Square Peg, Apex Capital Partners, and Addition. The funds will be used to expand Zeller's research and engineering centre, including hiring 18 new engineering positions to help the company achieve its goal of being a fully regulated business bank.Memory.ai, a company that creates AI-based applications, has received $14 million in a round headed by Melesio and Sanden. The money will be utilized to develop new apps that will aid individuals in becoming more productive. Dewo and Glue are two applications that are in the works. Both of these apps will be available in the second half of this year.Toronto-based Joyride, offering solutions for micromobility operators, has announced that it has raised an oversubscribed amount, $3.7M as demand and popularity for e-scooters, bikes and mopeds go up. Its software platform enables businesses to launch, manage and scale their scooter-sharing and bike-sharing businesses all in one place. Joyride stated that the funding would enable it to take its micromobility software to the next level, to thousands of new markets

In this top-performing research paper published by Aging on November 15, 2017, entitled, “Towards natural mimetics of metformin and rapamycin,” researchers used powerful screening methods to analyze over 800 natural compounds to assess their anti-aging potential and safety profile in an effort to mimic metformin and rapamycin. In 2017, researchers from the United States' Insilico Medicine, Inc. and Life Extension, the United Kingdom's Biogerontology Research Foundation, Canada's Queen's University, and Russia's Russian Academy of Sciences, worked together to test a new strategy to accelerate the development of safe, wide-scale anti-aging nutraceuticals. “One strategy to hasten the process has been the repurposing of existing, FDA-approved drugs that show off-label anti-cancer and anti-aging potential, and at the top of that list are metformin and rapamycin, two drugs that mimic caloric restriction.” To date, this paper has generated an Altmetric Attention score of 127. Altmetric Attention scores provide an at-a-glance indication of the volume and type of online attention the research has received. Top Aging publications rated by Altmetric score - https://www.aging-us.com/news_room/altmetric Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.101319 DOI - http://doi.org/10.18632/aging.101319 Full text - https://www.aging-us.com/article/101319/text Correspondence to: Alexander Aliper email: aliper@insilicomedicine.com Keywords: geroprotector, metformin, rapamycin, deep learning, natural, nutraceutical, compound screening, aging About Aging Launched in 2009, Aging publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at http://www.Aging-US.com​​ or connect with us on: Twitter - https://twitter.com/AgingJrnl​ Facebook - https://www.facebook.com/AgingUS/​ SoundCloud - https://soundcloud.com/aging-us​ YouTube - https://www.youtube.com/agingus​ LinkedIn - https://www.linkedin.com/company/aging​ Aging is published by Impact Journals, LLC please visit http://www.ImpactJournals.com​​ or connect with @ImpactJrnls Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

Today, Dr. ALEX ZHAVORONKOV, expert in artificial intelligence for drug discovery in aging research, CEO of Insilico Medicine, explains his current work in Human longevity, future repair shops for humans, his “Young AI” app for tracking your aging, and the value of thinking young!

In this twentieth episode, Polina Mamoshina introduces recently launched Deep Longevity, and its app (young.ai). Read the transcript Biomarkers of aging are introduced. She explains that they have taken a superior approach by using deep learning instead of machine learning. Aging clocks in general are covered. Finally, she shares her view that transcriptomic and proteonomic clocks are the likely future. Topics we discussed in this episode Personal background: Moscow State University, Oxford University, Insilico Medicine hackathon Bringing Deep Longevity out of stealth, Young.ai companion app Deep Longevity introduction including company aims Description of Young.AI app Biomarkers of aging as the accelerant of market for aging interventions Introduction to aging clocks: Horvath, Hannum Taking a novel and superior technological approach to aging clocks, using deep neural networks, instead of shallow machine learning Limitations of shallow machine learning models Ability of neural networks to capture highly non linear dependencies and what that matters for biological age determination Investing in anticipated payoff from deep learning over the long-term, even if machine learning may be good enough in many cases now Biological age prediction with Aging.ai Two approaches to designing aging clocks Machine learned PhenoAge biological age score Introducing mortality, with the GrimAge score Longevity clinics and life insurance as market Biological age scoring as onboarding tool for life insurance markets Training datasets Common blood analytes used in PhenoAge vs Aging.ai Optimized blood analyte levels for a given individual to get younger Orthodox medicine uses blood analyte levels that are not specific to the individual and not optimized ranges; designed to detect only late-stage pathologies Cheapness of regular blood analytes Emerging market is likely to age score bodily subsystems rather than provide an overall singular biological age score Goal is to find the fastest ticking clock in your body Biological age test using a selfie Providing a library of biological age scores, from free to expensive, so users can upgrade, find out more about themselves Belief that proteomic and transcriptomic clocks will outperform epigenetic clocks in terms of being actionable with interventions Epigenetics and aging Acceleration of the aging rate may show up "late" in terms of being able to intervene, on the epigenome Youthful blood plasma exchanges and age quantification Transcriptomic, proteomic, and glycomic clocks Anticipated rise of longevity clinics Show links Deep Longevity (Company Website) Insilico Medicine (Company Website) Human Longevity, Inc. (Company Website) Regent Pacific Group (Company Website) Young.AI (App from Deep Longevity) Aging.AI (Biological Age Prediction) 'DNA Methylation Age of Human Tissues and Cell Types' (Paper) 'Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research' (Paper) Steve Horvath (WikiPedia Entry) 'Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates' (Paper) Gregory Hannum (LinkedIn) Morgan Levine (LinkedIn) 'An epigenetic biomarker of aging for lifespan and healthspan' (Paper) Elysium Health (Company Website) 'DNA Methylation GrimAge Strongly Predicts Lifespan and Healthspan' (Paper) FOXO BioScience (Company Website) NHANES III (CDC) AgeoTypes (Stanford Article) GlycanAge (Company Website) GENOS (Company Website) 'Biohorology and Biomarkers of Aging: Current State-of-the-Art, Challenges and Opportunities' (Paper) 'Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity' (Paper) 'Deep Integrated Biomarkers of Aging' (Paper) 'Deep Biomarkers of Aging and Longevity: from Research to Applications' (Paper)

Most of us would define longevity in terms of the length of our lives. But what if there’s more to it than that? What if quality matters as much as quantity? What if we could extend our healthiest, most productive years?Alex Zhavoronkov is the Founder and CEO of Insilico Medicine, a venture-backed company that seeks to extend healthy longevity through innovative AI solutions for drug discovery and aging research. Insilico has R&D labs in Belgium, the UK, Korea, Russia, Hong Kong, the US and Taiwan, and in 2017, the company was named one of NVIDIA’s Top 5 AI companies in its potential for social impact.   On the inaugural episode of HLTH Matters, Alex joins Dr. Kuku and Dr. Shlain to discuss his work in the field of longevity biotechnology, describing his particular focus on extending our most productive years as opposed to the end of life. He introduces us to the new science around aging clocks and weighs in on the possibilities of using their data to reverse age-associated pathologies. Listen in for Alex’s insight around the potential for AI to influence our political systems and learn how a sole focus on rejuvenation rate would support global economic growth.Topics CoveredAlex’s transition from information technology to biopharmaThe intersection of technology and the human elementThe concept of quality-adjusted life years or QALYsAlex’s focus on extending productive years vs. end of lifeHow Alex sees longevity as more than just living longerThe science around aging clocks and their notable accuracyDeveloping actionable interventions from aging clock dataHow economic forces influence which diseases we researchEmbracing MMA principles as an approach to drug discoveryAlex’s work with triple-negative breast cancer moleculesAlex’s insight into how AI might influence political systemsThe parallels between gain of function and economic growthHow Peter Diamandis’ work gives Alex cause for optimism Connect with Alex ZhavoronkovInsilico MedicineAlex on LinkedIn Connect with Dr. Gulati, Dr. Shlain & Dr. KukuHLTH ConferenceDr. Shlain on LinkedInDr. Shlain on TwitterDr. Kuku on LinkedInDr. Kuku on TwitterResourcesDeep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric TopolThe Ageless Generation: How Advances in Biomedicine Will Transform the Global Economy by Alex ZhavoronkovSteven Horvath’s Aging Clock ResearchInsilico’s Partnership with CTFHAbundance: The Future is Better Than You Think by Peter H. Diamandis and Steven KotlerExponential Medicine

Alex Zhavoronkov is a particularly high level human being, originally working in the field of computer science, Alex had a realization that contributing to aging and aging research would be the biggest contribution he could make for humanity. Utilizing his expertise in computer science and artificial intelligence, he began using these skills to enter the world of longevity in a significant way. Artificial intelligence will play a massive role in drug and biomarker discovery going forward and Alex's company, Insilico Medicine is leading the charge in a serious way.It was an absolute honor to speak with Alex Zhavoronkov as he explained in simple terms just how exciting Insilico's tech is. How it works, all the different projects they are involved in, and just how much of a financial behemoth Insilico has the potential to be. (Keep an eye out for their IPO in the next few years)-James Ruhle, SimpleBioTechPodcast.comStay up to date with the latest episodes and BioTech updates by following me on instagram @SimpleBioTechIf you want to know which BioTech companies I'm currently excited about, connect with me on Angel List at Angel.co/jamesruhle

Experten-Interview mit PD Dr. med. Christoph Spinner, Infektiologe am Klinikum rechts der Isar und derzeit gefragter Experte zum Thema Coronavirus. Die COVID-19 Krise hat in den letzten Wochen den Alltag der Menschen auf der ganzen Welt drastisch verändert. Konferenzen, Konzerte und andere Großveranstaltungen werden abgesagt, Flüge werden gecancelt und Menschen tätigen Hamsterkäufe. Weltweit haben sich über 90,000 Menschen mit dem Virus angesteckt und es gibt 3,000 Tote. Wird sich die Situation weiter verschlimmern und müssen wir womöglich sogar mit einer anhaltenden, globalen Wirtschaftskrise rechnen? Oder wird das SARS-CoV-2, also das neue Coronavirus durch den Wärmeanstieg sich nicht weiter ausbreiten und schon in wenigen Wochen vergessen sein? Es gibt ja bereits vier Coronaviren, die in Menschen zirkulieren. Sie verursachen eine leichte Erkältung und wir haben keine Impfung für sie. Was macht das SARS-CoV-2 so viel gefährlicher? Es heißt ja, dass das Virus bei gesunden, jungen Menschen zumeist harmlos verläuft. Dennoch tauchen immer wieder Berichte von Verläufen auch bei jüngeren Menschen auf, die Lungenentzündungen entwickeln, die bis zum Tod führen können. Was sind die entscheidenden Faktoren die dafür sorgen, dass es zu einem schlimmen Verlauf kommt? Es gibt verschiedene Ansätze wie Artificial Intelligence helfen kann und soll, die COVID-19-Krise zu managen. Darunter „Blue Dot“, eine kanadische Firma, die frühzeitig anhand des Screenings und der Auswertung von Online-Daten einen Cluster von ungewöhnlichen Lungenentzündungen in der Region Wuhan identifiziert hat. Denken Sie, dass derartige AI-basierte Public Health Tools künftig den Ausbruch von Infektionen verhindern können? Eine andere AI Firma „BenevolentAI“ hat bekanntgegeben, das Baricitinib, ein Rheuma-Antikörper gegen das Coronavirus wirksam sein könnte. Insilico Medicine aus Hong Kong hat bekanntgegeben, dass ihr AI Algorithmus sechs neue Moleküle hergestellt hat, die die virale Replikation aufhalten können. Denken Sie, dass AI der Schlüssel in der Entwicklung neuer Therapien des Coronavirus sein könnten oder sind das nur nette Leuchtturmprojekte, während das Gros der Arbeit von klassischen Forschungsgruppen übernommen wird? Moderna Therapeutics, eine Biotechfirma aus Massachussets, gibt an, bereits zeitnah in der Lage zu sein einen COVID-19 Impfstoff zu produzieren und zu liefern, der auf mRNA basiert und den Körper dazu anregt, die entsprechenden Proteine herzustellen, die eine Immunreaktion hervorrufen. Sie sagen in einem anderen Interview, dass sie eher mit Jahren rechnen, die eine Entwicklung eine ordentlichen Impfstoffs braucht – was halten sie von dem Ansatz von Moderna Therapeutics? Die chinesischen Firmen Infervision und Alibaba haben jeweils AI-Systeme entwickelt, mit deren Hilfe COVID-19 diagnostiziert werden kann. Mithilfe von Brust-CT-Scans soll mit über 90%iger Treffsicherheit eine Abgrenzung gegenüber viralen Pneumonien erfolgen können. Die Entscheidung kann dabei in nur 20 Sekunden getroffen werden und das System wurde mit Daten von über 5,000 Corona-Fällen trainiert. Denken Sie, dass ein solcher Ansatz helfen wird, eine Pandemie mit SARS-CoV-2 zu verhindern?

Drug discovery is changing. Deep generative models, as being pioneered by our guest Dr. Alex Zhavoronkov, founder and CEO of InSilico Medicine, are able to generate entirely new molecules and even, as we'll hear, can discover new targets altogether. Within days. This fall, InSilico and collaborators published an application of their generative reinforcement learning (GENTRL) to generate tens of thousands of novel compounds, synthesized only the 6 best, and identified a selective and nanomolar-potent candidate among them. We discuss deep generative drug discovery models, promise and criticism on these new methodologies, and dive into new research from their pending publications on NASH and we hear about a breaking new contribution by the InSilico team regarding a potential treatment for CoronaVirus. This is the TomorrowScale Podcast. I'm Justin Briggs. Sources: “Deep learning enables rapid identification of potent DDR1 kinase inhibitors” (Zhavoronkov 2019) “Reply to: Assessing the Impact of Generative AI on Medicinal Chemistry” (Zhavoronkov 2020) https://insilico.com https://tomorrowscale.com The TomorrowScale Podcast was created by Justin Briggs to showcase scientists and entrepreneurs who are building the future, and hear stories about how to build the future. The views expressed by the host and guests are their own, and the content of this show should not be considered legal, tax, or investing advice. Thank you to our guests for sharing their time and knowledge with us. Thanks for listening. Please science responsibly. --- Support this podcast: https://anchor.fm/tomorrowscale/support

In this episode, our hosts discuss the shifting landscape of modern pharmaceuticals and how artificial intelligence, machine learning, DNA samples and 3D printing can impact how we combat illness and dispense medicine.Drug DiscoveryDiscovering a new drug can take decades, billions of dollars and untold research hours by some of the smartest people on the planet. The process often involves carrying out physical tests on enormous libraries of molecules. Even with the help of robotics, it’s an arduous process. Recent advances in deep learning have ignited new hope, as major pharmaceutical companies collaborate with AI-powered drug discovery startups.The Power of AIIn just 21 days, Insilico Medicine used AI to design a molecule that effectively targeted a protein involved in fibrosis, (the formation of excess fibrous tissue) found in mice. The results were achieved by leveraging two AI sub-fields: GANs and Reinforcement Learning.Individualized DiagnosisDNA analysis and genetic testing are increasingly playing a vital role in screening and determining the risk of developing certain diseases. In some cases, it is also being used to guide medical treatment. Different types of genetic testing are done for different reasons: Diagnostic testing, Pre-symptomatic and Predictive testing, Carrier testing, Pharmacogenetics, Prenatal testing, Newborn screening, and Pre-implantation testing.3D Printed PillsThe first, and as yet only, available 3D-printed medicine is made by Aprecia Pharmaceuticals. “Spritam” was approved in 2015 to control seizures brought on by epilepsy. Although in its infancy, 3D printing and the future of customized and decentralized manufacturing has exciting possibilities.Further Sources and Details can be found on Humanatronix.com

Quentin Vanhaelen is the Director of the Virtual Human Project at Insilico Medicine, a startup that has achieved a Series B funding for about $37 Million USD. They are using artificial intelligence and deep learning to identify bio markers in the human DNA to help aging research and medicine discovery that can help us extend longevity in the human race. Quentin has a Bachelor's & Masters degree in Physical Sciences and a PhD in Philosophy and Theoretical Physics, he's originally from Belgium he joins us today from Moscow to discuss the theories of aging, artificial intelligence and the applications to discover new techniques that can help us to discover more about aging and medicine. Click here to get resources and more info on this interview: https://www.rodrigoflamenco.com/Quentin-Vanhaelen-Insilico/ About Rodrigo Flamenco: Rodrigo flamenco started in a third world Country called El Salvador in Central America with no idea how to do businesses earning $600 a month on an IT job while on a $20K+ debt. After taking a couple of courses he decided to start his own business called Epic Web Studio, which ended working with many big international brands and over 18+ countries all over the world. Then an opportunity arrived to start a new business and for 2 years, without a website or even a name, that business won two $30K projects, one $55K project and connected with many of the top professionals and business and founders in the industry of animation. That business is now Frame Freak Studio which is his 100% focus. Now he helps other business (specially tech startups) to get better results through animations and helping professionals in the animation industry through teaching marketing. About Level Up! Your Business, Life & Mind: Level Up! is a series of interviews featuring successful professionals from all over the world who are making a mark in their lives and others and finding out how they did it so you can learn from the very best. Our interviews will be focused on finding out their principles, strategies, mindsets, habits and tools that you can use and implement in your business, life and mind to get to the next level of your goals. Join Our Business Community! http://www.rodrigoflamenco.com/ SUBSCRIBE! http://www.framefreakstudio.com/youtube Podcast: https://www.rodrigoflamenco.com/podcast/ Facebook: https://www.facebook.com/RodrigoeFlamenco/ Instagram: https://www.instagram.com/rodrigoeflamenco/ Linked In: https://www.linkedin.com/in/rodrigoflamenco/ Twitter: https://twitter.com/RodrigoFlamenco If you want to see better quality of videos please support us by becoming our Patron and help us bring you more amazing videos. https://www.rodrigoflamenco.com/patreon --- Send in a voice message: https://anchor.fm/level-up-business/message

Fedor Galkin, Project Manager at Insilico Medicine, Inc., discusses his work studying the microbiome, human genotypes, and aging/longevity. Galkin graduated from Moscow State University with a degree in Bioengineering & Bioinformatics. His work focuses on human microbiome aging clocks based on deep learning. Interestingly, the microbiome can serve as an incredibly accurate biological clock, able to predict the age of many people within just years. Galkin discusses the earliest microbiome aging clocks and recent advances, and the technology that is behind them. Some of these technologies can make assessments based on an individual's blood biochemistry and gene expression levels, etc., but as he states there has never before been a clock that predicts age based on gut microbes. Galkin discusses their work in detail, discussing how they select and look at the microbes. Galkin explains the correlations and organization in the microbes, and how with age, things fluctuate. He details how they observe the changes that show age, and how certain conditions, such as diabetes will make the gut microbes appear as a much older person. Continuing, the bioengineering expert talks about nutrients, and how supplements, etc. can impact the biological systems. And he explains how their work on the species level is ongoing, but that they hope to delve deeper into the functional and genetic level as well, in their continued study of the human microbiome. In this podcast: What is a microbiome aging clock? How nutrients play a role in the gut microbiome The role of supplements in biological health

Att ta fram nya läkemedel är både kostsamt och tidskrävande – det kan ta upp till tio år och kosta miljarder. I det senaste avsnittet av AI-podden får vi lära oss mer om hur medicinföretaget Insilico Medicine tillsammans med ett forskningsteam från universitetet i Toronto lyckades ta fram ett läkemedel på endast 46 dagar med hjälp av AI. Vi tittar även närmare på tjänster som gör det möjligt att byta ut olika personers ansikten på videoklipp har snabbt blivit populära. Appen ZAO är en av de mer populära apparna och har nyligen fått utstå en kritikerstorm mot dess användarvillkor som ger företaget "fria, oåterkalleliga, permanenta, överförbara och relicensierbara" rättigheter till allt användargenererat innehåll. Det kinesiska företaget bakom ZAO har därför gått ut med löfte om ändringar. Marc Zuckerberg har gjort uttalande på ämnet och säger att Facebook kämpar för att hitta ett sätt att hantera och navigera bland falska videos som blir allt vanligare och som han säger kan utgöra en helt ny kategori av desinformation. Vi ställer oss också frågan om AI kan lära sig kreativa processer som skrivande och författarskap. Kreativitet kan ses som en osynlig process som till och med kan vara svår för konstnärer och författare att själva förklara hur den går till. AI har kunnat lära sig att se mönster och kan också på det viset hjälpa kreatörer framåt i sitt arbete. Läs mer: https://ai-podden.se

The titan thought leader podcast discusses trends, & innovations through intriguing and insightful discussions with industry thought leaders in the biopharmaceutical industry. Episode 2 discusses the artificial intelligence in drug discovery and aging research with guest speaker, Andrei Zhavoronkov from Insilico Medicine.

Longevity, eternal youth or even immortality have been an aspiration in religion and culture throughout history. Today, people adopt all sorts of approaches to increase their wellbeing, delay aging and avoid diseases. Efforts are increasingly quantified with sensors, wearables, or even biohacking - interventions to influence body biology. The new hope for advancements in longevity is seen in artificial intelligence, which is becoming increasingly powerful. Alex Zhavoronkov has been researching the use of AI in aging for years. He is the CEO of Insilico Medicine, a Baltimore-based leader in the next-generation artificial intelligence technologies for drug discovery and aging biomarkers discovery. He truly is a well of knowledge - since 2012 he published over 130 peer-reviewed research papers and 2 books including "The Ageless Generation: How Biomedical Advances Will Transform the Global Economy" (Palgrave Macmillan, 2013). In this episode, he talks about the complexity of aging as a biological process, types of artificial intelligence and the role of AI in research advancements.   Some of his latest research articles include:  Blood Biochemistry Analysis to Detect Smoking Status and Quantify Accelerated Aging in Smokers - https://www.nature.com/articles/s41598-018-35704-w#author-information Artificial intelligence for aging and longevity research: Recent advances and perspectives - https://www.sciencedirect.com/science/article/pii/S156816371830240X?via%3Dihub Artificial Intelligence for Drug Discovery, Biomarker Development, and Generation of Novel Chemistry - https://pubs.acs.org/doi/10.1021/acs.molpharmaceut.8b00930 Listen also:  F013 What to expect from artificial intelligence in healthcare in the next 10 years? (Sally Daub, Enlitic) https://medium.com/faces-of-digital-health/f013-what-to-expect-from-artificial-intelligence-in-healthcare-in-the-next-10-years-fdaf2edf32f8

Artificial intelligence offers the promise of better health, faster drug discovery and testing, to create improved medical outcomes for patients. We talk with a world expert on using AI in life sciences to discover and develop drugs faster and less expensively.

Artificial intelligence offers the promise of better health, faster drug discovery and testing, to create improved medical outcomes for patients. We talk with a world expert on using AI in life sciences to discover and develop drugs faster and less expensively.

In today's episode, Kyle chats with Alexander Zhebrak, CTO of Insilico Medicine, Inc. Insilico self describes as artificial intelligence for drug discovery, biomarker development, and aging research. The conversation in this episode explores the ways in which machine learning, in particular, deep learning, is contributing to the advancement of drug discovery. This happens not just through research but also through software development. Insilico works on data pipelines and tools like MOSES, a benchmarking platform to support research on machine learning for drug discovery. The MOSES platform provides a standardized benchmarking dataset, a set of open-sourced models with unified implementation, and metrics to evaluate and assess their performance.

Informa’s Heather Granato talks with Alex Zhavoronkov, Ph.D., of Insilico Medicine, about the evolution of artificial intelligence, and how AI technology is being applied to investigate new ingredients that could beneficially impact the aging process, opening up new avenues for product development.

Insilico Medicine is working to harness artificial intelligence to address diseases of aging and in the process reinvent the way new drugs are discovered and developed. Its AI platform is integrated into the continuum of the discovery and development process and seeks to improve target identification, the selection of drug candidates, and predict clinical trial outcomes. In addition to working in collaboration with the large pharmaceutical companies, Insilico is pursuing internal drug discovery programs in range of diseases of aging. We spoke to Alex Zhavoronkov, CEO of Inisilco Medicine, about the company's platform technology, the potential for AI to transform the discovery and development of drugs, and why Insilico focuses its efforts on diseases of aging.

欢迎收听丽莎老师讲机器人，想要孩子参加机器人竞赛、创意编程、创客竞赛的辅导，找丽莎老师！欢迎添加微信号：153 5359 2068，或搜索微信公众号：我最爱机器人。丽莎老师讲机器人之AI能抗衰老？本月15日，Buck老龄化研究所、Insilico Medicine和Juvenescence公司宣布成立Napa Therapeutics，以开发针对一种新型老龄化相关靶标的药物。Buck老龄化研究所是世界领先的研究中心之一，专注于研究衰老并消除与年龄有关的疾病。Insilico Medicine是一家人工智能（AI）公司，专注于一系列与衰老相关的垂直行业。Juvenescence是一家致力于改善衰老和衰老相关疾病的医药公司。Napa Therapeutics的创立基于在Buck研究所总裁兼首席执行官埃里克·威尔第博士实验室中进行的NAD代谢的开创性研究。该实验室将与Napa合作，使用Insilico的药物研发引擎，来加速发现新化合物。威尔第博士表示：“这是利用尖端AI加速药物发现的独特机会。Buck很高兴能与Insilico和Juve nescence合作，共同努力为这个世代和后代消除与年龄有关的疾病的威胁。”“我对这种模式，以及将Buck研究所的优质科学与Insilico Medicine的卓越深度学习引擎相结合的能力感到非常兴奋。对我而言，这是使用AI 人工智能与HI（人类智慧）来获取两者最佳的演变进程的又一个重要进步，”Juvenescence首席执行官 格雷戈 瑞贝利博士说：“Napa Therapeutics使Juve nescence加深了与Buck研究所和Insilico Medicine的合作。我们希望能缩短识别可用于临床的分子所需的时间，最重要的是惠及患者。”Insilico Medicine的创始人兼首席执行官表示：他们非常高兴与Buck研究所和Juvenescence合作，围绕着被医药行业所忽视的一系列非常有前景的靶标。老龄化研究是无私的，它将改善和延长地球上每个人的生命，减少与年龄相关疾病的痛苦。

My guest on the podcast is Anastasia Georgievskaya, General Manager at Youth Laboratories. She’s the co-founder and General Manager at Youth Laboratories, a company developing tools to study aging and discover effective anti-aging interventions using advances in machine vision and artificial intelligence. Anastasia has a degree in bioengineering and bioinformatics from the Moscow State University. She won numerous math and bioinformatics competitions and successfully volunteered for some of the most prestigious companies in aging research including Insilico Medicine, which I interviewed earlier on this podcast about its product innovations.She helped develop an app for tracking age-related facial changes and was one of the driving forces to organize the first beauty competition judged by the robot jury, Beauty.AI.This inspired me – not because of the topic – but because of the transformational effects technologies such as AI are starting to have on our day to day life. What triggers me is what we could learn from examples like this to inspire other forms of value creation. Hence I invited Anastacia to my podcast. We explore the value of her company’s product innovation beyond the point of beauty. What lessons have been learned, what are the essentials to get right, and is the potential for society at large.Here are some of Anastasia’s quotes.The company's story started with the Beauty.AI contest, and then...It's a beauty competition judged by Artificial Intelligence, the first one in the world.We believe that tracking your skin health and the biomarkers that can be seen on your face is very relevant because images are a very cheap source of data and it's very affordable. If you want to track the skin condition and track the dynamics, you need to make sure you can track its in‑dynamicsalgorithms can adjust to your baseline, and then you would be able to track the effects of different changes on your skin. For example, your nutrition, your lifestyle, amount of sleep, weather, sports, only you can understand what's the most beneficial lifestyle for you.It's very well‑aligned to the trend of personalization During this interview, you will learn three things:That AI will change our approach to many questions – and as such spark new ideas for creating value we currently don’t have an idea aboutWhy collaboration is key to not only accelerate the innovation process but more importantly, give you insights to increase the value you offer with your solutionHow involving skeptics increase the relevancy and simplicity of your solution. See acast.com/privacy for privacy and opt-out information.

QCR Global Group is a niche life science management consulting firm, hosting thought leaders on an array of topics in industry. With a primary mission of, providing enlightened academic exchange with change innovators to maximize the cross professional understanding of methods while improving the exchange of information that benefits industry experts and their consumers. Hosted by Dr. D. Orozco, President of the Life Science Practice and Principal Manager at QCR, each thought leader provides a quick explanation of what he/she specializes in, challenges and benefits based on case representation, along with insightful strategies from their angle of the profession.

QCR Global Group is a niche life science management consulting firm, hosting thought leaders on an array of topics in industry. With a primary mission of, providing enlightened academic exchange with change innovators to maximize the cross professional understanding of methods while improving the exchange of information that benefits industry experts and their consumers. Hosted by Dr. D. Orozco, President of the Life Science Practice and Principal Manager at QCR, each thought leader provides a quick explanation of what he/she specializes in, challenges and benefits based on case representation, along with insightful strategies from their angle of the profession.

My guest on the podcast this week is Alex Zhavoronkov, CEO of Insilico MedicineOn a day to day basis, Alex is the CEO of Insilico Medicine (www.insilico.com), which is focused exclusively on developing and applying deep learning methods to drug discovery. It’s probably the largest next-gen AI and bioinformatics company in the world focusing exclusively on aging and age-related diseases.Alex is also the director of the Biogerontology Research Foundation and the founder of the International Aging Research Portfolio. He heads the laboratory of regenerative medicine at the Center for Pediatric Hematology-Oncology and Immunology and is the adjunct professor at the Buck Institute for Research in Aging in Novato, California and the international adjunct professor at the Moscow Institute of Physics and Technology.As an anti-aging expert, he is convinced that even people past their 70s, who are in good health, should set their longevity expectations to live past 150. It is a realistic goal considering the current longevity records and progress in technology. Stretching longevity expectations may help delay or reverse the psychological aging.This inspired me to invite Alex to my podcast, to explore how technology can be used to accelerate progress in this field, how it can augment researchers around the world to create breakthroughs that will ultimately increase longevity for all of us.We discuss the big idea behind his company to extend healthy productive longevity, first by understanding the size of the challenge, from there exploring how technology can help to address the challenge, and if applied the right way, the magnitude of the impact it could create. Here are some quotes: “Aging is one of the major challenges that humanity is facing today. The population has tripled over the past 70 years, and the population also got older.”“We need to identify new ways to keep people in their optimal healthy state for as long as possible, just to ensure that the economy remains intact.” “There is lots and lots of data available for aging research, but AI takes it to the next level. It basically accelerates everything. Think about this as a carriage versus Formula 1.”“If you are pursuing aging research and you find a way to extend the life of everybody on the planet by one year, you generate seven billion, well, seven‑and‑a‑half billion, quality-adjusted life years. That is really the scale we're talking about."By listening to this podcast you will learn the following:1) Why the best innovations start with the end goal in mind2) How, by clearly defining your Business Model upfront, you can avoid delays and unpleasant surprises3) Why data privacy is becoming a critical aspect of innovation success.4) And why it’s key to surround yourself with like-minded people who share the same passion, and are not just in for the money. See acast.com/privacy for privacy and opt-out information.

Today's episode on Redefining Medicine features Alex Zhavoronkov, PhD. As CEO of Insilico Medicine, Zhavoronkov is a pioneer in artificial intelligence, deep learning techniques, and blockchain technologies for drug discovery and biomarker development. With a long-held belief that the issue of aging needs to be further researched and addressed, Zhavoronkov seeks to discover how precision medicine can further advance treatment protocols for patients. Through research focuses on genomics, epigenetics, and personalized screening techniques, Zhavoronkov works on advancing Regenerative and Anti-Aging Medicine with the most cutting-edge technologies and clinical research.

In this episode Dr. Dan welcomes another young Russian researcher that is focused on aging. Specifically she is focused on skin aging. Her name is Polina Mamoshina and she works for a bioinformatics company by the name of Insilico Medicine, Inc. Her research fosus is on skin aging biomarkers and photoaging of the skin. We discuss why sunscreen might be a very bad idea, what can we do now to slow or correct skin aging, and what does the future hold for reversing skin aging.

    Alex Zhavoronkov, PhD, is a head of the Regenerative Medicine Laboratory at the Federal Clinical Research Center for Pediatric Hematology, Oncology and Immunology in and the adjunct professor of the Moscow Institute of Physics and Technology. He is also a director and trustee of the Biogerontology Research Foundation, a UK-based registered charity supporting  aging research worldwide and a director of the International Aging Research Portfolio (IARP) knowledge management project. He is the CEO or Insilico Medicine, Inc, a Baltimore-based company with research facility on the campus of the Johns Hopkins University Emerging Technology Centers. The company is focusing on signaling pathway activation analysis, drug discovery and drug repurposing for aging and age-related diseases. He also heads NeuroG, a neuroinformatics project intended to assist the elderly suffering from dementia. His primary research interests include systems biology of aging, regenerative medicine, next-generation  sequencing, molecular diagnostics and pathway analysis. He is the author of "The Ageless Generation: How advances in biomedicine will transform the global economy" (Palgrave Macmillan, 2013).  He holds two Bachelor Degrees from Queen’ s University, a Masters in Biotechnology from Johns Hopkins University, and a PhD in Biophysics  from the Moscow State University.