The DOI matches its Crossref record; 142 of the 147 references it cites were found in a registry or an index.
Unleashing the power of generative AI in drug discovery
Open accessAbstract
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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“In addition to the clinical applications of AI, generative AI is also revolutionizing diabetes treatment by accelerating the drug discovery process (59).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [59]
Citations
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Cited by
Sorted by citations
Serrano, D. R., Luciano, F. C., Anaya, B. J., Öngoren, B., Kara, A., Molina, G., Ramirez, B. I., Sánchez-Guirales, S. A., Simon, J. A., Tomietto, G., et al. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16101328 · Cited by 594
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Self-citationGangwal, A., & Lavecchia, A. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104360 · Cited by 113
Self-citationA Scoping Review of Artificial Intelligence for Precision Nutrition
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Metadata
- Authors
- Gangwal, A., & Lavecchia, A.
- Affiliations
- Shri Vile Parle Kelavani Mandal (IN)
- University of Naples Federico II (IT)
- Corresponding author
- Lavecchia, Antonio
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 29
- Issue
- 6
- Pages
- 103992
- ISSN
- 1359-6446 (ISSN-L)
- 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
- 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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