[2406.07709] Diagnosing and fixing common problems in Bayesian optimization for molecule design
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Computer Science > Machine Learning

arXiv:2406.07709 (cs)
[Submitted on 11 Jun 2024 (v1), last revised 25 Jul 2024 (this version, v2)]

Title:Diagnosing and fixing common problems in Bayesian optimization for molecule design

Authors:Austin Tripp, José Miguel Hernández-Lobato
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Abstract:Bayesian optimization (BO) is a principled approach to molecular design tasks. In this paper we explain three pitfalls of BO which can cause poor empirical performance: an incorrect prior width, over-smoothing, and inadequate acquisition function maximization. We show that with these issues addressed, even a basic BO setup is able to achieve the highest overall performance on the PMO benchmark for molecule design (Gao et al 2022). These results suggest that BO may benefit from more attention in the machine learning for molecules community.
Comments: 8 pages, 4 figures. ICML 2024 AI for science workshop (this https URL). Code at: this https URL
Subjects: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Machine Learning (stat.ML)
Cite as: arXiv:2406.07709 [cs.LG]
  (or arXiv:2406.07709v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2406.07709

Submission history

From: Austin Tripp [view email]
[v1] Tue, 11 Jun 2024 20:44:04 UTC (173 KB)
[v2] Thu, 25 Jul 2024 14:17:40 UTC (173 KB)
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