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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rnn monte carlo tree search deep neural network models optimizing the octanolwater partition coefficient variational autoencoders design of organic materials python library chemts chemical space algorithm molecular design blackbox optimization recurrent neural networks predetermined fragments

Xiufeng Yang,Jinzhe Zhang,Kazuki Yoshizoe,Kei Terayama,Koji Tsuda,.ChemTS: an efficient python library for de novo molecular generation. 18 (1),.

Xiufeng Yang,Jinzhe Zhang,Kazuki Yoshizoe,Kei Terayama,Koji Tsuda,"ChemTS: an efficient python library for de novo molecular generation" 18

Xiufeng Yang,Jinzhe Zhang,Kazuki Yoshizoe,Kei Terayama,Koji Tsuda,"ChemTS: an efficient python library for de novo molecular generation"