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AI in Pharma R&D: Bridging Breakthroughs and Future Frontiers
Dmitry A Kudlay1,2,3, Oleg V Satyshev4, Natalia P Doktorova5,6
1Department of Pharmacology, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University (Sechenov University); 119991, Moscow, Russia.
Current Drug Discovery Technologies
|August 11, 2026
Summary
Artificial intelligence (AI) accelerates drug discovery and improves diagnostics by identifying drug candidates, optimizing synthesis, and enhancing disease detection accuracy. AI integration in medicine offers significant advancements but raises ethical and data quality considerations.
Area of Science:
- Pharmacology and Medical Informatics
- Biotechnology and Drug Development
Background:
- Artificial intelligence (AI) is increasingly utilized in scientific research and development.
- AI applications span drug discovery, repurposing, biomarker identification, and diagnostic tools.
Purpose of the Study:
- To review the application of AI in creating and repurposing drugs.
- To explore AI's role in discovering biomarkers and shortening diagnostic timelines.
- To examine AI's impact on drug development, formulation, and intelligent diagnostics.
Main Methods:
- Review of AI applications in pharmaceutical research and development.
- Analysis of AI's contribution to identifying drug candidates, virtual screening, and synthesis optimization.
- Examination of AI-driven diagnostic tools for accuracy and efficiency.
Main Results:
- AI significantly reduces the time for identifying new drug candidates through rapid biotarget identification and virtual screening.
- AI optimizes drug candidates based on predictive pharmacokinetic and toxicological data.
- AI-powered diagnostics demonstrate high accuracy and time efficiency in oncology, cardiology, and other medical fields.
Conclusions:
- AI revolutionizes drug discovery and development by accelerating candidate identification and optimization.
- AI enhances medical diagnostics, offering more accurate and timely disease detection and risk assessment.
- The expanding implementation of AI in medicine necessitates addressing ethical concerns, data quality, safety, and workforce adaptation.
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