Issue 34, 2019

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 consistently find more desirable molecules for the studied tasks in relatively short time. We hope that our method can support medicinal chemists in accelerating and improving the lead optimization process.

Graphical abstract: Efficient multi-objective molecular optimization in a continuous latent space

Supplementary files

Article information

Article type
Edge Article
Submitted
18 apr 2019
Accepted
02 iyl 2019
First published
08 iyl 2019
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2019,10, 8016-8024

Efficient multi-objective molecular optimization in a continuous latent space

R. Winter, F. Montanari, A. Steffen, H. Briem, F. Noé and D. Clevert, Chem. Sci., 2019, 10, 8016 DOI: 10.1039/C9SC01928F

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