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.

Graphical Abstract

Keywords:

• Molecular design
• Monte Carlo tree search
• recurrent neural network
• python library

Classification:

• 60 New topics/Others
• 404 Materials informatics / Genomics

Acknowledgements

We would like to thank Hou Zhufeng, Diptesh Das, Masato Sumita and Thaer M. Dieb for their fruitful discussions.

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the ‘Materials research by Information Integration’ Initiative (MI2I) project and Core Research for Evolutional Science and Technology (CREST) [grant number JPMJCR1502] from Japan Science and Technology Agency (JST). It was also supported by Grant-in-Aid for Scientific Research on Innovative Areas ‘Nano Informatics’ [grant number 25106005] from the Japan Society for the Promotion of Science (JSPS). In addition, it was supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT) as ‘Priority Issue on Post-K computer’ (Building Innovative Drug Discovery Infrastructure Through Functional Control of Biomolecular Systems).