Cited by 265Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and EducationMachine Learning in Materials Science

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Assessing the impact of generative AI on medicinal chemistry

Walters, W. Patrick, Murcko, Mark·2020·Nature Biotechnology

DOI 10.1038/s41587-020-0418-2

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Authors: Walters, W. P., & Murcko, M.
Affiliations: Relay Therapeutics (United States) (US)
Corresponding author: Walters, W. Patrick
ORCID: Walters, W. Patrick
Murcko, Mark
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2020-01-30
Volume: 38
Issue: 2
Pages: 143-145
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-020-0418-2
OpenAlex: W3003906593
Cited by: 262
References: 23
Citations by year: 2026: 60 · 2025: 40 · 2024: 43 · 2023: 35 · 2022: 21 · 2021: 37 · 2020: 25
Type: dissertation
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords: generative AI, medicinal chemistry, drug discovery, structure-based drug design, reinforcement learning, adenosine A2A receptor, AI impact assessment
MeSH terms: Artificial Intelligence, Chemistry, Pharmaceutical, Humans, Inhibitory Concentration 50, Quantitative Structure-Activity Relationship, Cell Line, Tumor, Protein Kinase Inhibitors, Drug Discovery

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