Preferential Multi-Objective Bayesian Optimization for Drug Discovery

Tai Dang, Long-Hung Pham, Sang T. Truong, Ari Glenn, Wendy Nguyen, Edward A Pham, Jeffrey S. Glenn, Sanmi Koyejo, Thang Luong

Track: long paper (up to 10 pages)

Keywords: Drug Discovery, Virtual Screening, Molecular Docking, Bayesian Optimization, Preference Learning, Human Feedback, Diffusion Models

TL;DR: Leveraging Bayesian Preferential Learning and diffusion-based docking models to enhance multi-objective virtual screening by incorporating chemists' preferences.

Abstract: Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hit selection, a step where chemists manually select a few promising molecules based on their chemical intuition. This creates a major bottleneck in the virtual screening process for drug discovery, demanding experts to repeatedly balance complex trade-offs among drug properties across a vast pool of candidates. To improve the efficiency and reliability of this process, we propose a novel human-centered framework named CheapVS that allows chemists to guide the ligand selection process by providing preferences regarding the trade-offs between drug properties via pairwise comparison. Our framework combines preferential multi-objective Bayesian optimization with a docking model for measuring binding affinity to capture human chemical intuition for improving hit identification. Specifically, on a library of 100K chemical candidates targeting EGFR and DRD2, CheapVS outperforms state-of-the-art screening methods in identifying drugs within a limited computational budget. Notably, our method can recover up to 16/37 EGFR and 37/58 DRD2 known drugs while screening only 6% of the library, showcasing its potential to significantly advance drug discovery.

Submission Number: 3