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Cited by 180Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and EducationProtein Degradation and Inhibitors

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 access
KP Jayatunga, Madura, Ayers, Margaret, Bruens, Lotte, Jayanth, Dhruv, Meier, Christoph·2024·Drug Discovery Today
DOI 10.1016/j.drudis.2024.104009
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Abstract

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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Crossref · 10.1016/j.drudis.2024.104009

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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)
DOI
10.1016/j.drudis.2024.104009
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
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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