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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| Published in | Science and technology of advanced materials Vol. 18; no. 1; pp. 972 - 976 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Taylor & Francis
31.12.2017
Taylor & Francis Ltd Taylor & Francis Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1468-6996 1878-5514 1878-5514 |
| DOI | 10.1080/14686996.2017.1401424 |
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| 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
. |
|---|---|
| AbstractList | 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.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. 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 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. 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. 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 . |
| Author | Yoshizoe, Kazuki Terayama, Kei Tsuda, Koji Yang, Xiufeng Zhang, Jinzhe |
| Author_xml | – sequence: 1 givenname: Xiufeng surname: Yang fullname: Yang, Xiufeng organization: Graduate School of Frontier Sciences, The University of Tokyo – sequence: 2 givenname: Jinzhe surname: Zhang fullname: Zhang, Jinzhe organization: Department of Biosciences, INSA Lyon – sequence: 3 givenname: Kazuki surname: Yoshizoe fullname: Yoshizoe, Kazuki organization: RIKEN, Center for Advanced Intelligence Project – sequence: 4 givenname: Kei surname: Terayama fullname: Terayama, Kei organization: Graduate School of Frontier Sciences, The University of Tokyo – sequence: 5 givenname: Koji surname: Tsuda fullname: Tsuda, Koji email: tsuda@k.u-tokyo.ac.jp organization: National Institute for Materials Science |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29435094$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 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 |
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| Title | ChemTS: an efficient python library for de novo molecular generation |
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