Deep reinforcement learning in chemistry : A review
© 2024 Wiley Periodicals LLC.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 45(2024), 22 vom: 15. Juli, Seite 1886-1898 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Review drug discovery molecule generation molecule geometry optimization reinforcement learning |
| Zusammenfassung: | © 2024 Wiley Periodicals LLC. Reinforcement learning (RL) has been applied to various domains in computational chemistry and has found wide-spread success. In this review, we first motivate the application of RL to chemistry and list some broad application domains, for example, molecule generation, geometry optimization, and retrosynthetic pathway search. We set up some of the formalism associated with reinforcement learning that should help the reader translate their chemistry problems into a form where RL can be used to solve them. We then discuss the solution formulations and algorithms proposed in recent literature for these problems, the advantages of one over the other, together with the necessary details of the RL algorithms they employ. This article should help the reader understand the state of RL applications in chemistry, learn about some relevant actively-researched open problems, gain insight into how RL can be used to approach them and hopefully inspire innovative RL applications in Chemistry |
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| Beschreibung: | Date Revised 03.07.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.27354 |