Challenges of Therapeutics Machine Learning in the Wild - Kexin Huang

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live: https://valence-discovery.github.io/M2D2-meetings/ Title: Challenges of Therapeutics Machine Learning in the Wild Abstract: Machine learning for therapeutics offer incredible opportunities for expansion, innovation, and impact. Despite promises, many challenges exist. In this talk, the speaker will first highlight two high-impact but relatively understudied directions - ML-aided clinical trial design and low-data/cross-context biomedicine. Then, he will discuss challenges arising from therapeutics ML adoption in the wild, namely, generating actionable hypotheses and user interface with domain scientists. Lastly, challenges in infrastructure, such as data and benchmark, will be looked at. Speaker: Kexin Huang - https://www.kexinhuang.com/ Twitter Prudencio: https://twitter.com/tossouprudencio Twitter Therence: https://twitter.com/Therence_mtl Twitter Cas: https://twitter.com/cas_wognum Twitter Valence Discovery: https://twitter.com/valence_ai ~ Chapters: 00:00 Introduction 01:01 Predicting clinical trial outcomes before it starts 10:08 Q&A 16:22 Low-data and cross-context system biomedicine 28:46 Generating actionable hypotheses 34:13 Interface with domain scientists 41:11 Therapeutic Data Commons (TDC) 45:08 Q&A 50:15 Benchmarks in TDC 58:37 Q&A 1:02:22 Outro