Uncertainty-guided drug discovery
Invited talk, From Models to Molecules: AI’s Expanding Roles in Therapeutics, hosted by Novoprotein, South San Francisco, CA
Drug discovery is expensive largely because we must make decisions under uncertainty – about biological mechanisms, assay noise, and the ultimate clinical success of a molecule. I will present three complementary tools for measuring and managing these unknowns: (1) diversity-steered sampling, which identifies when large language models are guessing, making literature triage and hypothesis generation more reliable; (2) semiparametric conformal prediction, which wraps around any predictive model to deliver calibrated predictions for many correlated assay read-outs; and (3) multi-objective Bayesian optimization with multivariate ranks, which translates these calibrated predictions into action by balancing potency, selectivity, and manufacturability along a principled Pareto frontier. Together, these methods can accelerate and de-risk hit finding, safety assessment, and lead optimization. I will close by discussing how uncertainty-aware methods can move from benchmark to bench through close collaboration between experimentalists and AI practitioners. [Event Page] 
