The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons
Open accessAbstract
AI techniques are making inroads into the field of drug discovery. As a result, a growing number of drugs and vaccines have been discovered using AI. However, questions remain about the success of these molecules in clinical trials. To address these questions, we conducted a first analysis of the clinical pipelines of AI-native Biotech companies. In Phase I we find AI-discovered molecules have an 80-90% success rate, substantially higher than historic industry averages. This suggests, we argue, that AI is highly capable of designing or identifying molecules with drug-like properties. In Phase II the success rate is ∼40%, albeit on a limited sample size, comparable to historic industry averages. Our findings highlight early signs of the clinical potential of AI-discovered molecules.
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In the citing paper
“AI-discovered drugs have experienced similar levels of phase 2 trial failure as non-AI-discovered drugs20,21”
From A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial (2025) · cites this paper as [21]
In the citing paper
“Despite substantial progress in artificial intelligence (AI) for generative chemistry, few novel AI-discovered or AI-designed drugs have reached human clinical trials.”
From A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial (2025) · cites this paper as [21]
In the citing paper
“none has so far progressed through phase 3 trials21.”
From A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial (2025) · cites this paper as [21]
Citations
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Sorted by citations
Huanbutta, K., Burapapadh, K., Kraisit, P., Sriamornsak, P., Ganokratanaa, T., Suwanpitak, K., & Sangnim, T. (2024) · European Journal of Pharmaceutical Sciences · doi:10.1016/j.ejps.2024.106938 · Cited by 192
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“AI-discovered drugs have experienced similar levels of phase 2 trial failure as non-AI-discovered drugs 20,21 , and none has so far progressed through phase 3 trials 21 .”
Hallmarks of artificial intelligence contributions to precision oncology
Chang, T., Park, S., Schäffer, A. A., Jiang, P., & Ruppin, E. (2025) · Nature Cancer · doi:10.1038/s43018-025-00917-2 · Cited by 83
Recent advances in AI-driven protein-ligand interaction predictions
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Metadata
- Authors
- KP Jayatunga, M., Ayers, M., Bruens, L., Jayanth, D., & Meier, C.
- Affiliations
- Boston Consulting Group (United States) (US)
- Corresponding author
- Meier, Christoph
- ORCID
- Meier, Christoph
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 29
- Issue
- 6
- Pages
- 104009
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W4396521393
- Cited by
- 166
- References
- 20
- Citations by year
- 2026: 71 · 2025: 76 · 2024: 18
- FWCI
- 34.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- www.sciencedirect.com
- Licence
- Locations
- Drug Discovery Today (publishedVersion, cc-by-nc-nd)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- drug discovery, clinical trial success rates, drug-likeness, machine learning in drug discovery
- MeSH terms
- Artificial Intelligence, Clinical Trials as Topic, Drug Industry, Humans, Drug Discovery
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