Academic Journal
AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.
| Τίτλος: | AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype. |
|---|---|
| Συγγραφείς: | Khairil, Lisa, Benny, Koay Hean Seng, Jerry, Jesreena, Khatib, Farhat Mussa, Che Ramli, Muhammad Danial, Kumar, Suresh |
| Πηγή: | Pharmaceuticals (14248247); Jun2026, Vol. 19 Issue 6, p916, 43p |
| Θεματικοί όροι: | Regulatory compliance, Treatment failure, Reproducible research, Protein structure prediction, Clinical trials, Access to information |
| Περίληψη: | The integration of artificial intelligence (AI) into the life sciences has accelerated significantly between 2022 and 2026, accompanied by global investment exceeding USD 100 billion and widespread expectations of a transformative impact in drug discovery. Despite these advances, the extent to which AI has improved clinical outcomes remains unclear. This study presents a structured narrative review evaluating the economic, technical, clinical, and regulatory dimensions of AI adoption in drug discovery. Current evidence indicates that clinical attrition rates remain high, with approximately 90% of drug candidates entering clinical development failing to achieve regulatory approval. Although AI systems such as AlphaFold have achieved high structural prediction accuracy, with predicted local distance difference test (pLDDT) scores exceeding 90 for well-structured proteins and root mean square deviation (RMSD) values comparable to experimental methods, limitations persist in modelling protein dynamics, post-translational modifications, and protein–ligand interactions. Clinical case studies demonstrate that while AI can accelerate early-stage discovery timelines, these advantages do not consistently translate into improved late-stage success rates. Furthermore, reproducibility challenges, limited data transparency, and regulatory gaps continue to constrain reliable implementation. These findings suggest that AI in drug discovery is currently in a transitional phase characterised by high investment but limited validated clinical impact. Future progress will depend on strengthening validation frameworks, improving data sharing practices, and aligning regulatory standards with real-world clinical performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Pharmaceuticals (14248247) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Biomedical Index |
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| Header | DbId: edm DbLabel: Biomedical Index An: 194907176 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.76538085938 |
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| Items | – Name: Title Label: Title Group: Ti Data: AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khairil%2C+Lisa%22">Khairil, Lisa</searchLink><br /><searchLink fieldCode="AR" term="%22Benny%2C+Koay+Hean+Seng%22">Benny, Koay Hean Seng</searchLink><br /><searchLink fieldCode="AR" term="%22Jerry%2C+Jesreena%22">Jerry, Jesreena</searchLink><br /><searchLink fieldCode="AR" term="%22Khatib%2C+Farhat+Mussa%22">Khatib, Farhat Mussa</searchLink><br /><searchLink fieldCode="AR" term="%22Che+Ramli%2C+Muhammad+Danial%22">Che Ramli, Muhammad Danial</searchLink><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Suresh%22">Kumar, Suresh</searchLink> – Name: TitleSource Label: Source Group: Src Data: Pharmaceuticals (14248247); Jun2026, Vol. 19 Issue 6, p916, 43p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Regulatory+compliance%22">Regulatory compliance</searchLink><br /><searchLink fieldCode="DE" term="%22Treatment+failure%22">Treatment failure</searchLink><br /><searchLink fieldCode="DE" term="%22Reproducible+research%22">Reproducible research</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+structure+prediction%22">Protein structure prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+trials%22">Clinical trials</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+information%22">Access to information</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The integration of artificial intelligence (AI) into the life sciences has accelerated significantly between 2022 and 2026, accompanied by global investment exceeding USD 100 billion and widespread expectations of a transformative impact in drug discovery. Despite these advances, the extent to which AI has improved clinical outcomes remains unclear. This study presents a structured narrative review evaluating the economic, technical, clinical, and regulatory dimensions of AI adoption in drug discovery. Current evidence indicates that clinical attrition rates remain high, with approximately 90% of drug candidates entering clinical development failing to achieve regulatory approval. Although AI systems such as AlphaFold have achieved high structural prediction accuracy, with predicted local distance difference test (pLDDT) scores exceeding 90 for well-structured proteins and root mean square deviation (RMSD) values comparable to experimental methods, limitations persist in modelling protein dynamics, post-translational modifications, and protein–ligand interactions. Clinical case studies demonstrate that while AI can accelerate early-stage discovery timelines, these advantages do not consistently translate into improved late-stage success rates. Furthermore, reproducibility challenges, limited data transparency, and regulatory gaps continue to constrain reliable implementation. These findings suggest that AI in drug discovery is currently in a transitional phase characterised by high investment but limited validated clinical impact. Future progress will depend on strengthening validation frameworks, improving data sharing practices, and aligning regulatory standards with real-world clinical performance. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Pharmaceuticals (14248247) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/ph19060916 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 43 StartPage: 916 Subjects: – SubjectFull: Regulatory compliance Type: general – SubjectFull: Treatment failure Type: general – SubjectFull: Reproducible research Type: general – SubjectFull: Protein structure prediction Type: general – SubjectFull: Clinical trials Type: general – SubjectFull: Access to information Type: general Titles: – TitleFull: AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khairil, Lisa – PersonEntity: Name: NameFull: Benny, Koay Hean Seng – PersonEntity: Name: NameFull: Jerry, Jesreena – PersonEntity: Name: NameFull: Khatib, Farhat Mussa – PersonEntity: Name: NameFull: Che Ramli, Muhammad Danial – PersonEntity: Name: NameFull: Kumar, Suresh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14248247 Numbering: – Type: volume Value: 19 – Type: issue Value: 6 Titles: – TitleFull: Pharmaceuticals (14248247) Type: main |
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