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Bridging Computation and Experimentation with Evidential Deep Learning | Ava Amini

If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live: https://m2d2.io/talks/m2d2/about/ Also consider joining the M2D2 Slack: https://m2d2group.slack.com/join/shar... Title: Bridging Computation and Experimentation with Evidential Deep Learning Abstract: The success of machine learning (ML) in chemistry and biology is contingent on experimental efforts that generate relevant datasets and that validate model predictions. Critically, such experimentation often necessitates significant time and resource investments. Computational methods to inform experimental modeling could help alleviate this burden and bridge the gap between computational predictions and experimental validation. In this talk, I will discuss how ML algorithms that quantify prediction uncertainties could meet this critical need. Using molecular property prediction and drug discovery as a motivating use case, I will present a new method -- evidential deep learning -- for uncertainty quantification in neural networks and demonstrate its potential to (1) achieve calibrated estimates of model uncertainty, (2) improve sample efficiency via uncertainty-guided active learning, and (3) inform experimental validation via targeted virtual screening. I will close by highlighting how prediction uncertainty can accelerate and guide key steps in experimental lifecycles, opening the door for sustained feedback between computation and experimentation in the chemical and biological sciences. Speakers: Ava Amini -   / avapamini   Twitter Prudencio:   / tossouprudencio   Twitter Therence:   / therence_mtl   Twitter Cas:   / cas_wognum   Twitter Valence Discovery:   / valence_ai   ~ Chapters: 00:00 - Intro 05:04 - Motivation: Uncertainty to Bridge Experimentation and Computation 15:32 - Evidential Learning to Accelerate Molecular Discovery 17:47 - Uncertainty Estimation via Ensembling or Sampling 20:29 - Evidential Deep Learning 28:35 - Formulating Evidential DL Predictions 33:34 - Uncertainty Calibration for Molecular Property Prediction 39:18 - Evidential Uncertainty for Guided Learning 47:01 - AI-Driven Discovery of New Antibiotics 53:19 - Q&A