A Tale of Many Tails: Multi-Objective Bayesian Optimization for Molecular Design
Date:
Active design of therapeutic molecules requires the joint optimization of multiple, potentially competing properties. Multi-objective Bayesian optimization (MOBO) offers a sample-efficient framework for identifying Pareto-optimal drug candidates. MOBO proceeds in cycles, a single iteration of which involves (1) sampling molecules from a combinatorially vast design space, (2) inferring multiple properties of interest, and (3) selecting the most promising subset for wet-lab evaluation. In this talk, I highlight the importance of modeling the tails – extreme, low-probability events –- in biological applications and propose algorithms designed to accommodate complex tail behavior in each of these steps. Together, the algorithms enable modeling flexibility beyond that afforded by the common log-concave (e.g., Gaussian) assumption. [Event Page]