Issue 12, 2019

A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

Abstract

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimization of log P values with a constraint for synthetic accessibility and shows that the GA is as good as or better than the ML approaches for this particular property. The molecules found by the GB-GA bear little resemblance to the molecules used to construct the initial mating pool, indicating that the GB-GA approach can traverse a relatively large distance in chemical space using relatively few (50) generations. The paper also introduces a new non-ML graph-based generative model (GB-GM) that can be parameterized using very small data sets and combined with a Monte Carlo tree search (MCTS) algorithm. The results are comparable to previously published results (Sci. Technol. Adv. Mater., 2017, 18, 972–976) using a recurrent neural network (RNN) generative model, and the GB-GM-based method is several orders of magnitude faster. The MCTS results seem more dependent on the composition of the training set than the GA approach for this particular property. Our results suggest that the performance of new ML-based generative models should be compared to that of more traditional, and often simpler, approaches such a GA.

Graphical abstract: A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

Supplementary files

Article information

Article type
Edge Article
Submitted
01 12 2018
Accepted
08 2 2019
First published
11 2 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 license

Chem. Sci., 2019,10, 3567-3572

A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

J. H. Jensen, Chem. Sci., 2019, 10, 3567 DOI: 10.1039/C8SC05372C

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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