Preferential Multi-Objective Bayesian Optimization for Drug Discovery

• URL: http://arxiv.org/abs/2503.16841v1
• Date: Fri, 21 Mar 2025 04:27:06 GMT
• Title: Preferential Multi-Objective Bayesian Optimization for Drug Discovery
• Authors: Tai Dang, Long-Hung Pham, Sang T. Truong, Ari Glenn, Wendy Nguyen, Edward A. Pham, Jeffrey S. Glenn, Sanmi Koyejo, Thang Luong,
• Abstract summary: CheapVS provides preferences regarding the trade-offs between drug properties via pairwise comparison.<n>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.
• Score: 13.740630847478453
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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