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|a 10.1002/jcc.26984
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|a eng
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|a Jiang, Runxuan
|e verfasserin
|4 aut
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|a Conformer-RL
|b A deep reinforcement learning library for conformer generation
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a Date Completed 20.09.2022
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|a Date Revised 19.10.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2022 The Authors. Journal of Computational Chemistry published by Wiley Periodicals LLC.
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|a Conformer-RL is an open-source Python package for applying deep reinforcement learning (RL) to the task of generating a diverse set of low-energy conformations for a single molecule. The library features a simple interface to train a deep RL conformer generation model on any covalently bonded molecule or polymer, including most drug-like molecules. Under the hood, it implements state-of-the-art RL algorithms and graph neural network architectures tuned specifically for molecular structures. Conformer-RL is also a platform for researching new algorithms and neural network architectures for conformer generation, as the library contains modular class interfaces for RL environments and agents, allowing users to easily swap components with their own implementations. Additionally, it comes with tools to visualize and save generated conformers for further analysis. Conformer-RL is well-tested and thoroughly documented with tutorials for each of the functionalities mentioned above, and is available on PyPi and Github: https://github.com/ZimmermanGroup/conformer-rl
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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|a conformer generation
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|a graph neural network
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|a machine learning
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| 650 |
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|a reinforcement learning
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|a Polymers
|2 NLM
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|a Gogineni, Tarun
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|a Kammeraad, Joshua
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|a He, Yifei
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|a Tewari, Ambuj
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|a Zimmerman, Paul M
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 43(2022), 27 vom: 15. Okt., Seite 1880-1886
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