ChemTS: an efficient python library for de novo molecular generation

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

研究成果: ジャーナルへの寄稿学術誌査読

116 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)972-976
ページ数5
ジャーナルScience and Technology of Advanced Materials
18
1
DOI
出版ステータス出版済み - 12月 31 2017
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • 材料科学(全般)

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