ChemTS: an efficient python library for de novo molecular generation

Abstract

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

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Cited by papers we hold 5

• Chemical space exploration: how genetic algorithms find the needle in the haystack · 2020
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• Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models · 2020
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• Constrained Bayesian optimization for automatic chemical design using variational autoencoders · 2020
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• Self-assembly as a key player for materials nanoarchitectonics · 2019
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• A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space · 2019
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