Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning-Reference-Cited by-同舟云学术

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Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Published:2024-04-10 Issue:6 Volume:4 Page:2160-2172
ISSN:2691-3704
Container-title:JACS Au
language:en
Short-container-title:JACS Au

Author:

Guo Jeff¹²^ORCID,Schwaller Philippe¹²

Affiliation:

1. Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland

2. National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland

Funder

NCCR Catalysis

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/jacsau.4c00066

Reference99 articles.

1. High-throughput property-driven generative design of functional organic molecules

2. Ultra-large library docking for discovering new chemotypes

3. Deep learning enables rapid identification of potent DDR1 kinase inhibitors

4. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

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