Artificial intelligence in drug discovery: recent advances and future perspectives
Open accessAbstract
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
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Other reviews have focused on the perspectives of healthcare professionals on topics not directly involving patient care, such as research and drug development.
“Other reviews have focused on the perspectives of healthcare professionals on topics not directly involving patient care, such as research and drug development [13, 14].”
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- Authors
- Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Boehringer Ingelheim (Germany) (DE)
- Corresponding author
- Jiménez-Luna, José
- Grisoni, Francesca
- Weskamp, Nils
- Schneider, Gisbert
- Venue
- Expert Opinion on Drug Discovery
- Publisher
- Informa UK Limited
- Published
- 2021-04-02
- Volume
- 16
- Issue
- 9
- Pages
- 949-959
- ISSN
- 1746-0441 (ISSN-L)
- 1746-045X
- OpenAlex
- W3143491409
- Cited by
- 464
- References
- 10
- Citations by year
- 2026: 88 · 2025: 121 · 2024: 128 · 2023: 64 · 2022: 42 · 2021: 21
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Expert Opinion on Drug Discovery (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (publishedVersion, cc-by-nc-nd)
- ETH Zürich Research Collection
- Funders
- Swiss National Science Foundation (205321_182176)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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
- artificial intelligence in drug discovery, deep learning, cheminformatics, QSAR, structure-based drug design, de novo molecular design, message passing model, open data sharing
- MeSH terms
- Machine Learning, Artificial Intelligence, Humans, Drug Design, Quantitative Structure-Activity Relationship, Drug Discovery
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