Determining best practices for using genetic algorithms in molecular discovery

Greenstein, Brianna L.; Elsey, Danielle C.; Hutchison, Geoffrey R.

doi:10.1063/5.0158053

Determining best practices for using genetic algorithms in molecular discovery

Journal Article · Fri Sep 01 00:00:00 EDT 2023 · Journal of Chemical Physics

DOI:https://doi.org/10.1063/5.0158053· OSTI ID:2246912

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University of Pittsburgh, PA (United States); University of Pittsburgh
University of Pittsburgh, PA (United States)

Genetic algorithms (GAs) are a powerful tool to search large chemical spaces for inverse molecular design. However, GAs have multiple hyperparameters that have not been thoroughly investigated for chemical space searches. In this tutorial, we examine the general effects of a number of hyperparameters, such as population size, elitism rate, selection method, mutation rate, and convergence criteria, on key GA performance metrics. Here, we show that using a self-termination method with a minimum Spearman’s rank correlation coefficient of 0.8 between generations maintained for 50 consecutive generations along with a population size of 32, a 50% elitism rate, three-way tournament selection, and a 40% mutation rate provides the best balance of finding the overall champion, maintaining good coverage of elite targets, and improving relative speedup for general use in molecular design GAs.

View Accepted Manuscript (DOE)

Research Organization:: University of Pittsburgh, PA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0019335

OSTI ID:: 2246912

Journal Information:: Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 9 Vol. 159; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

36 MATERIALS SCIENCE
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Chemical space
Computational chemistry
Data mining
Evolutionary computation
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Molecular properties
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Polarizability
Polymers
Statistical analysis

Determining best practices for using genetic algorithms in molecular discovery

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References (47)

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