This project investigates the comparative analysis of various acquisition function methods on the efficiency of Bayesian Optimization (BO) in the drug discovery process, particularly focusing on small, diverse, unbalanced, and noisy datasets. The study will evaluate the impact of different acquisition functions, molecular featurization methods, and applicability domain (AD) across multiple drug discovery datasets to uncover optimal strategies and best practices for employing acquisition functions (AF) effectively in drug discovery challenges.

See also our final submission post on LinkedIn!

Slides are available at https://suneelbvs.github.io/AC-BO-Hackathon.html.

References:

1. Hugo Bellamy (2022), “Batched Bayesian Optimization for Drug Design in Noisy Environments.”, J Chem Inf Model. 2022 Sep 12; 62(17): 3970–3981..