ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery-Reference-Cited by-同舟云学术

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery

Published:2024-08-02 Issue:15 Volume:64 Page:5900-5911
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Bou Albert¹²^ORCID,Thomas Morgan¹,Dittert Sebastian¹,Navarro Carles²,Majewski Maciej²^ORCID,Wang Ye³^ORCID,Patel Shivam⁴,Tresadern Gary⁵^ORCID,Ahmad Mazen⁵,Moens Vincent⁶,Sherman Woody⁴^ORCID,Sciabola Simone³,De Fabritiis Gianni¹²⁷^ORCID

Affiliation:

1. Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain

2. Acellera Labs, C Dr. Trueta 183, 08005, Barcelona, Spain

3. Biogen Research and Development, 225 Binney Street, Cambridge, Massachusetts 02142, United States

4. Psivant Therapeutics, 451 D Street, Boston, Massachusetts 02210, United States

5. In Silico Discovery, Janssen Research & Development, Janssen Pharmaceutica N. V., Turnhoutseweg 30, B-2340 Beerse, Belgium

6. PyTorch Team, Meta, 11−21 Canal Reach, London, N1C 4DB, United Kingdom

7. Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Spain

Funder

Agentschap Innoveren en Ondernemen

Center for Information Technology

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00895

Reference56 articles.

1. Deep learning for molecular design—a review of the state of the art

2. Deep learning approaches for de novo drug design: An overview

3. GuacaMol: Benchmarking Models for de Novo Molecular Design

4. Molecular de-novo design through deep reinforcement learning

5. Deep reinforcement learning for de novo drug design