Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties

TL;DR

This paper introduces a surrogate-based black-box optimization method using Gaussian Process Regression to efficiently optimize costly molecular properties, addressing generalization issues in molecular AI applications.

Contribution

It presents a novel optimization framework combining surrogate modeling and evolutionary algorithms for molecular property optimization, improving efficiency over traditional metaheuristics.

Findings

Successfully optimized a costly molecular property faster than pure metaheuristics.

Demonstrated effectiveness of Gaussian Process Regression as a surrogate model.

Addresses generalization issues in AI-driven molecular optimization.

Abstract

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.