Issue 38, 2024

Augmenting genetic algorithms with machine learning for inverse molecular design

Abstract

Evolutionary and machine learning methods have been successfully applied to the generation of molecules and materials exhibiting desired properties. The combination of these two paradigms in inverse design tasks can yield powerful methods that explore massive chemical spaces more efficiently, improving the quality of the generated compounds. However, such synergistic approaches are still an incipient area of research and appear underexplored in the literature. This perspective covers different ways of incorporating machine learning approaches into evolutionary learning frameworks, with the overall goal of increasing the optimization efficiency of genetic algorithms. In particular, machine learning surrogate models for faster fitness function evaluation, discriminator models to control population diversity on-the-fly, machine learning based crossover operations, and evolution in latent space are discussed. The further potential of these synergistic approaches in generative tasks is also assessed, outlining promising directions for future developments.

Graphical abstract: Augmenting genetic algorithms with machine learning for inverse molecular design

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Article information

Article type
Perspective
Submitted
03 5 2024
Accepted
09 9 2024
First published
11 9 2024
This article is Open Access

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

Chem. Sci., 2024,15, 15522-15539

Augmenting genetic algorithms with machine learning for inverse molecular design

H. Kneiding and D. Balcells, Chem. Sci., 2024, 15, 15522 DOI: 10.1039/D4SC02934H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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