Efficient Multi-Objective Molecular Optimization in a Continuous Latent Space

In this work, we propose a novel method that combines in silico prediction of molecular properties such as biological activity or pharmacokinetics with an in silico optimization algorithm, namely Particle Swarm Optimization. Our method takes a starting compound as input and proposes new molecules with more desirable (predicted) properties. It navigates a machine-learned continuous representation of a drug-like chemical space guided by a de fined objective function. The objective function combines multiple in silico prediction models, de fined desirability ranges and substructure constraints. We demonstrate that our proposed method is able to consistently fi nd more desirable molecules for the studied tasks in relatively short time.