Sample efficient reinforcement learning with active learning for molecular design-Reference-Cited by-同舟云学术

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Sample efficient reinforcement learning with active learning for molecular design

Published:2024 Issue:11 Volume:15 Page:4146-4160
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Dodds Michael¹^ORCID,Guo Jeff¹^ORCID,Löhr Thomas¹^ORCID,Tibo Alessandro¹^ORCID,Engkvist Ola¹^ORCID,Janet Jon Paul¹^ORCID

Affiliation:

1. Molecular AI, Discovery Sciences, R&D, AstraZeneca, 431 50, Gothenburg, Sweden

Abstract

Active learning accelerates the design of molecules during generative reinforcement learning by creating surrogate models of expensive reward functions, obtaining a 4- to 64-fold reduction in computational effort per hit.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC04653B

Reference91 articles.

1. The Matter Simulation (R)evolution

2. The Chemical Space Project

3. Role of computer-aided drug design in modern drug discovery

4. SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules

5. An open-source drug discovery platform enables ultra-large virtual screens

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Generative artificial intelligence for small molecule drug design;Current Opinion in Biotechnology;2024-10

2. Machine learning-aided generative molecular design;Nature Machine Intelligence;2024-06-18

3. Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning;JACS Au;2024-04-10

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