Tim Hoctor’s Post

"AI for drug discovery can hit at all the inefficiencies in every step of the drug design process." - Surya Ganguli, a16z venture partner and Professor at Stanford University. Recently, Surya and Bowen Liu, a16z investment partner, joined Vijay Pande, PhD to go deep on AI in Bio. The trio dove into many aspects of AI in life sciences including the potential applications of self-supervised learning, which allows AI to learn from vast amounts of unlabelled data, drastically improving the prediction and design of new drugs. Listen on Raising Health: https://lnkd.in/dzjQ3Bux

A potential game changer for drug discovery! Google DeepMind's new AI model, #AlphaProteo, leverages their prior work from #AlphaFold (which predicted the structure of over 100 million proteins). The new model is intended to "create proteins that bind effectively to target proteins." This is a link to the Perplexity page summarizing this news: https://lnkd.in/gzC94jBj And here's DeepMind's full write up: https://lnkd.in/gjB9BqxA

Natural product drug discovery comes with various historical limitations. Here at Kapoose Creek Bio, we have built UnEarth Rx, an integrated proprietary AI platform that is purpose-built to overcome those limitations and accelerate natural product drug discovery by: ✅ Accessing and characterizing never-before-seen natural chemistry ✅ Screening for activity, target and mechanism across diverse diseases in a single experiment ✅ Creating better, optimized drug leads using deep learning models ✅ Rapidly scaling for downstream R&D Through the integration of AI-powered UnEarth Rx, Kapoose Creek Bio has delivered exciting new leads in priority disease areas, including three proprietary assets undergoing hit-to-lead optimization for use in treating neurodegenerative diseases and depression. To learn more: https://lnkd.in/gZPjSDUe #biotech #innovation #drugdiscovery #AI

@Artificial Intelligence, Deep Learning, Machine Learning

Scientists at Massachusetts Institute of Technology unveil #SPARROW, an AI for smarter drug discovery! 💊 🔬 🎯 🎯 This quantitative decision-making framework uses AI to pick the best molecules for new drugs, prioritizing molecules based on utility and synthesis costs. Quick Read: https://lnkd.in/gGHNQGGk #bioinformatics #drugdiscovery #structuralbiology #ai #sciencenews

Excited to discuss the intersection of #AI, #drugdiscovery, and #networkscience with Giulia Menichetti, faculty at Harvard Medical School, Brigham and Women's Hospital and the Network Science Institute, in the Institute for Experiential AI at Northeastern University's webinar on Friday, March 1, 2024 at 1 PM EDT. Tune in as we explore: 🚀 What #networkscience is and how it is advancing #AI applications in #drugdiscovery 🧬 What is needed to realize the potential of AI in treating complex diseases 📈 What is ahead for AI and #lifesciences   🔑 The importance of wet-lab-in-the-loop  🤝 How we help partners advance diagnostics, drug discovery and more After our talk, Prof Menichetti and I will hold a live Q&A with the audience! Register for free here: https://bit.ly/3OWklpb

Welcome to 𝐓𝐡𝐞 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐞 𝐀𝐈 𝐎𝐝𝐲𝐬𝐬𝐞𝐲! Today, we delve into an overview of 𝐡𝐨𝐰 𝐰𝐞 𝐚𝐫𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 𝐛𝐲 𝐥𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐀𝐈. 𝐀𝐧𝐭𝐢𝐛𝐨𝐝𝐲 𝐃𝐞𝐬𝐢𝐠𝐧: Drug design remains a challenging endeavor due to the complexity of the involved design space. Artificial Intelligence provides a unique shot at the problem due to its capability of enabling efficient horizon exploration. We are applying our AI capabilities in 𝐬𝐜𝐫𝐞𝐞𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐝𝐞 𝐧𝐨𝐯𝐨 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐚𝐧𝐭𝐢𝐛𝐨𝐝𝐢𝐞𝐬 and small molecule drugs against challenging targets such as intrinsically disordered pathogenic proteoforms involved in Parkinson’s disease. 𝐁𝐢𝐨𝐩𝐡𝐲𝐬𝐢𝐜𝐬 𝐗 𝐃𝐞𝐞𝐩𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Deep Learning has revolutionized many fields but still remains in the nascent stages for drug design. Models like AlphaFold provide a strong foundation for the field but end-to-end solutions need to go far beyond. Hence, 𝐰𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩 𝐧𝐨𝐯𝐞𝐥 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬, built using biophysics principles at the core. 𝐌𝐮𝐥𝐭𝐢𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐚𝐫𝐲 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞: Our team consists of an ideal mix of members having strong experience in 𝐛𝐨𝐭𝐡 𝐭𝐡𝐞 𝐟𝐢𝐞𝐥𝐝𝐬 𝐨𝐟 𝐛𝐢𝐨𝐩𝐡𝐲𝐬𝐢𝐜𝐬 𝐚𝐧𝐝 𝐀𝐈. We strongly believe in the importance of a multidisciplinary team for tackling the problem. #deeplearning #drugdiscovery #ai #pharma #startup #machinelearning

🧬 Breakthrough in AI-Driven Drug Discovery: AlphaFold 3 Unveiled! 🚀 Google DeepMind and Isomorphic Labs have introduced AlphaFold 3, a game-changing AI that models DNA, RNA, and facilitates drug development. Here's why it's revolutionary: 🔬 50% more accurate than traditional methods in predicting biomolecular interactions 🧪 No pre-existing structural data needed 🏥 Accelerates research in medicine, agriculture, and materials science 💊 Streamlines drug development processes 🖥️ Accessible via AlphaFold Server for scientists worldwide At Allmatics, we're thrilled to see AI advancements in #healthtech. AlphaFold 3 represents a quantum leap in the industry, potentially saving millions of research years and drastically reducing costs in structural biology. This evolution in health-related #AI is incredibly inspiring. We're excited to continue our #development at the intersection of AI/ML/DS and Healthtech. Want to learn more about AlphaFold 3 and our insights on AI in #healthcare? Check out the links below! Google DeepMind blog: https://lnkd.in/etiXfCw8 AI + Healthcare Integration: https://lnkd.in/eCnkZgdK #AIinHealthcare #DrugDiscovery #AlphaFold3 #GoogleDeepMind

Aragen’s scientists, Pratima Srivastava, Vice President, Discovery Biology Solutions and Dr. Satinder Singh, Associate Director – DMPK along with Vyas M. Shingatgeri, Dean, School of Biosciences, Apeejay Stya University, in their latest review article published in Artificial Intelligence in Health, AccScience Publishing, titled “Revolutionizing new drug discovery: Harnessing AI and machine learning to overcome traditional challenges and accelerate targeted therapies”, shared comprehensive insights on how AI and ML are revolutionizing the drug discovery process. The article highlights the challenges of traditional methods, such as low success rates in clinical trials, and the role of AI-powered tools in transforming the field. By analyzing large datasets and using generative AI to design novel compounds, these technologies help optimize drug efficacy, predict human toxicity, and accelerate the development of targeted therapies. As a digital technology focused CRO/CDMO, Aragen is a pioneer in adopting AI and ML tools across its research, development and manufacturing operations. The article discusses both the breakthroughs and the hurdles in adopting AI/ML in drug discovery, offering insights into the future of precision medicine. Click here https://lnkd.in/gnNSKY37 to read the full article. #ai #machinelearning #aiinpharma #aiinhealth #cdmo #cro #pharmaservices #futureofpharma #drugdiscovery #dmpk

Drug Discovery: The Power of Artificial Intelligence and Biology’. Drug Discovery: The Power of Artificial Intelligence and Biology Artificial intelligence (AI) is becoming an essential building block in the field of drug discovery, serving as a bridge between biology and technology. Although this process requires meticulous organisation and an orderly sequence of steps, AI is proving to be an invaluable tool. From data collection to model validation, AI can help identify candidates for new drugs. Despite the successes, it is important to keep in mind the limitations and the need for rigorous laboratory testing and clinical trials. However, the combination of AI and biology promises to open up new opportunities for advancing medicine and disease treatment. #SpinQuantumTech, #Biology, #ArtificialIntelligence, #InnovationMedicine