ChemTS: an efficient python library for de novo molecular generation

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 net...

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Veröffentlicht in:Science and technology of advanced materials Jg. 18; H. 1; S. 972 - 976
Hauptverfasser: Yang, Xiufeng, Zhang, Jinzhe, Yoshizoe, Kazuki, Terayama, Kei, Tsuda, Koji
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Taylor & Francis 31.12.2017
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
404 Materials informatics / Genomics
60 New topics/Others
Algorithms
Artificial neural networks
Computer simulation
Design optimization
Fragments
Libraries
Molecular design
Monte Carlo tree search
Neural networks
New topics/Others
Organic materials
Python
python library
recurrent neural network
Recurrent neural networks
60 New topics/Others
Molecular design
Monte Carlo tree search
recurrent neural network
python library
404 Materials informatics / Genomics
ISSN:1468-6996, 1878-5514
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:1468-6996
1878-5514
DOI:10.1080/14686996.2017.1401424