Physics-Informed Bayesian Optimization for Conformational Ensemble Augmentation

Conformational search is a key part of reaction modeling, molecular docking, and other fields of computational chemistry, where it is important to take molecules' flexibility into account. However, modern conformational search approaches provide no guarantee that they did not miss any important conformation. Thus, identifying missing conformations from an existing ensemble is of broad importance for computational chemistry. In this paper, we introduce a Bayesian optimization algorithm for conformational ensemble augmentation, that is, locating missing conformers in an existing ensemble, which employs Bayesian optimization with physics-informed torsion-potential-based kernel function and a novel acquisition function that prioritizes potential energy surface exploration for increased conformer diversity. The devised method demonstrates high efficiency on a test set of biologically relevant molecules.