Cited by 202Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

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Unleashing the power of generative AI in drug discovery

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Gangwal, Amit, Lavecchia, Antonio·2024·Drug Discovery Today

DOI 10.1016/j.drudis.2024.103992

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Abstract

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing precision, reducing timelines and costs, and enabling AI-driven computer-aided drug design. This review focuses on recent advancements in deep generative models (DGMs) for de novo drug design, exploring diverse algorithms and their profound impact. It critically analyses the challenges that are intricately interwoven into these technologies, proposing strategies to unlock their full potential. It features case studies of both successes and failures in advancing drugs to clinical trials with AI assistance. Last, it outlines a forward-looking plan for optimizing DGMs in de novo drug design, thereby fostering faster and more cost-effective drug development.

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Authors: Gangwal, A., & Lavecchia, A.
Affiliations: Shri Vile Parle Kelavani Mandal (IN)
University of Naples Federico II (IT)
Corresponding author: Lavecchia, Antonio
ORCID: Gangwal, Amit
Lavecchia, Antonio
Venue: Drug Discovery Today
Publisher: Elsevier BV
Published: 2024-06
Volume: 29
Issue: 6
Pages: 103992
ISSN: 1359-6446 (ISSN-L)
DOI: 10.1016/j.drudis.2024.103992
OpenAlex: W4395053803
Cited by: 195
References: 147
Citations by year: 2026: 95 · 2025: 77 · 2024: 23
FWCI: 5.46
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Drug Discovery Today (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Governo Italiano Ministero dell'Istruzione dell'Universita e della Ricerca
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords: generative AI, drug discovery, deep generative models, de novo drug design, computer-aided drug design, clinical trial candidates
MeSH terms: Drug Development, Algorithms, Artificial Intelligence, Humans, Drug Design, Computer-Aided Design, Drug Discovery

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Unleashing the power of generative AI in drug discovery

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