(642e) Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design

Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high-fidelity simulations have driven the development of machine-learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a principled framework for sample-efficient search, while generative models provide a mechanism to propose novel, diverse candidates beyond fixed libraries. However, existing methods that couple the two often rely on continuous latent spaces, which introduces both architectural entanglement and scalability challenges.

This work introduces an alternative, modular “generate-then-optimize” framework for de novo multi-objective molecular design. At each iteration, a generative model constructs a large, diverse pool of candidate molecules, after which a novel acquisition function, qPMHI (multi-point Probability of Maximum Hypervolume Improvement), selects a batch of candidates most likely to expand the Pareto front. The key insight is that qPMHI decomposes additively, enabling exact, scalable batch selection through simple probability ranking estimated via Monte Carlo sampling. We benchmark the framework against state-of-the-art latent-space and discrete molecular-optimization methods, demonstrating significant improvements across synthetic benchmarks and a case study in sustainable energy storage, where our approach rapidly identifies novel, diverse, and high-performing quinone-based cathode materials for aqueous redox-flow batteries.