Efficient multi-objective molecular optimization in a continuous latent space.

ABSTRACT: One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable properties. 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 defined objective function. The objective function combines multiple in silico prediction models, defined desirability ranges and substructure constraints. We demonstrate that our proposed method is able to consiste

SUBMITTER: Winter R 

PROVIDER: S-EPMC6836962 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature