  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.
