Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties
This work addresses the challenge of expensive evaluations in molecular optimization for drug and material discovery, though it appears incremental as it builds on existing surrogate and evolutionary methods.
The authors tackled the problem of optimizing costly molecular properties by proposing a surrogate-based black-box optimization method that combines Gaussian Process Regression with an evolutionary algorithm, resulting in faster optimization compared to purely metaheuristic approaches.
AI-assisted molecular optimization is a very active research field as it is expected to provide the next-generation drugs and molecular materials. An important difficulty is that the properties to be optimized rely on costly evaluations. Machine learning methods are investigated with success to predict these properties, but show generalization issues on less known areas of the chemical space. We propose here a surrogate-based black box optimization method, to tackle jointly the optimization and machine learning problems. It consists in optimizing the expected improvement of the surrogate of a molecular property using an evolutionary algorithm. The surrogate is defined as a Gaussian Process Regression (GPR) model, learned on a relevant area of the search space with respect to the property to be optimized. We show that our approach can successfully optimize a costly property of interest much faster than a purely metaheuristic approach.