ChemTS: an efficient python library for de novo molecular generation

Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, Koji Tsuda

Research output: Contribution to journalArticlepeer-review

147 Citations (Scopus)


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

Original languageEnglish
Pages (from-to)972-976
Number of pages5
JournalScience and Technology of Advanced Materials
Issue number1
Publication statusPublished - Dec 31 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Materials Science(all)


Dive into the research topics of 'ChemTS: an efficient python library for de novo molecular generation'. Together they form a unique fingerprint.

Cite this