Interpretable Fragment‐Based Molecule Design with Self‐Learning Entropic Population Annealing

Abstract

Self‐learning entropic population annealing (SLEPA) is a recently developed method used for achieving interpretable black‐box optimization via density‐of‐states estimation. Applying SLEPA to a chemical space is not straightforward, however, because of its dependence on Markov chain Monte Carlo sampling in the space of generated entities. Herein, SLEPA is applied to optimal molecule generation by combining an irreducible Markov chain in the space of fragment multisets and a probabilistic fragment assembler such as MoLeR. The weighted samples from SLEPA are used to identify salient fragments for the highest occupied molecular orbitals‐lowest unoccupied molecular orbitals (HOMO‐LUMO) gap maximization and minimization, and the relationship between the identified fragments and the electronic structures is elucidated. This approach offers a viable platform to reconcile the incompatible goals of optimization and interpretation during molecular design.

Keywords

• black-box optimization
• density of states
• fragment assembly
• molecule generation