Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet
Open accessAbstract
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.
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The impact of big data and AI in drug discovery continues to attract significant interest from investors, industrial and academic scientists, and legislators.
“The impact of big data and AI in drug discovery, the subject of a 2020 review in this journal (4), continues to attract significant interest: Investors (5, 6), industrial (7, 8) and academic scientists (9, 10), and legislators (https://bit.ly/40LRAip) discuss the impact of AI for drug discovery (AI4DD).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [9]
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Trapotsi, M., Barrett, I. P., Mervin, L., Afzal, A. M., Sturm, N., Engkvist, O., & Bender, A. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12571241 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Venugopalan, S. et al. () It's easy to fool yourself: case studies on identifying bias and confounding in bio-medical datasets. Preprint at: https://arxiv.org/abs/1912.07661 · arXiv
Cited by
Sorted by citations
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
“The impact of big data and AI in drug discovery, the subject of a 2020 review in this journal (4), continues to attract significant interest: Investors (5, 6), industrial (7, 8) and academic scientists (9, 10), and legislators (https://bit.”
BackgroundBender, A., & Cortes-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.11.037 · Cited by 232 · see citing works
Self-citationSuccess stories of AI in drug discovery - where do things stand?
Mak, K.-K., Balijepalli, M. K., & Pichika, M. R. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.1985108 · Cited by 51 · see citing works
- Authors
- Bender, A., & Cortés-Ciriano, I.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- Corresponding author
- Bender, Andreas
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 26
- Issue
- 2
- Pages
- 511-524
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W3113150977
- Cited by
- 405
- References
- 96
- Citations by year
- 2026: 114 · 2025: 74 · 2024: 69 · 2023: 66 · 2022: 46 · 2021: 36
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Discovery Today (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Molecular Biology Laboratory
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, drug discovery, clinical success rate, drug efficacy, drug safety, clinical trial design, target selection, data generation
- MeSH terms
- Drug Development, Animals, Artificial Intelligence, Clinical Trials as Topic, Decision Making, Humans, Drug Discovery
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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