Bayesian Optimization for Molecules Should Be Pareto-Aware

• URL: http://arxiv.org/abs/2507.13704v2
• Date: Mon, 21 Jul 2025 10:52:25 GMT
• Title: Bayesian Optimization for Molecules Should Be Pareto-Aware
• Authors: Anabel Yong, Austin Tripp, Layla Hosseini-Gerami, Brooks Paige,
• Abstract summary: Multi-objective Bayesian optimization (MOBO) provides a principled framework for navigating trade-offs in molecular design.<n>We benchmark a simple MOBO strategy against a simple fixed-weight scalarized baseline using Expected Improvement (EI)<n>Our results show that even strong deterministic instantiations can underperform in low-data regimes.
• Score: 6.877358955271652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-objective Bayesian optimization (MOBO) provides a principled framework for navigating trade-offs in molecular design. However, its empirical advantages over scalarized alternatives remain underexplored. We benchmark a simple Pareto-based MOBO strategy -- Expected Hypervolume Improvement (EHVI) -- against a simple fixed-weight scalarized baseline using Expected Improvement (EI), under a tightly controlled setup with identical Gaussian Process surrogates and molecular representations. Across three molecular optimization tasks, EHVI consistently outperforms scalarized EI in terms of Pareto front coverage, convergence speed, and chemical diversity. While scalarization encompasses flexible variants -- including random or adaptive schemes -- our results show that even strong deterministic instantiations can underperform in low-data regimes. These findings offer concrete evidence for the practical advantages of Pareto-aware acquisition in de novo molecular optimization, especially when evaluation budgets are limited and trade-offs are nontrivial.

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