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Artificial Intelligence-driven Drug Discovery: Transforming Pharmaceutical Innovation Through Computational
Affan Ahmad1, Priyanshu Nema2, Shashi Kiran Mishra1
1School of Pharmaceutical Sciences, Chhatrapati Shahu Ji Maharaj University, Kanpur, UP, 208024, India.
Mini Reviews in Medicinal Chemistry
|May 19, 2026
Summary
Artificial intelligence (AI) and computational tools are revolutionizing drug discovery, accelerating the identification of effective and safe new medicines. These advanced methods reduce costs and failure rates in pharmaceutical development.
Area of Science:
- Pharmaceutical Sciences
- Computational Biology
- Genomics
Background:
- Drug discovery is complex and costly, with high failure rates due to issues like low efficacy and toxicity.
- Traditional methods face significant roadblocks in innovation despite scientific advances.
Purpose of the Study:
- To review the transformative role of AI and computational technologies in modern drug discovery and development.
- To highlight emerging approaches like drug repurposing and genomic therapies.
Main Methods:
- Integration of artificial intelligence (AI) models, predictive algorithms, and machine learning techniques.
- Application of computational approaches for drug repurposing.
- Utilizing genomic therapies and precision medicine.
Main Results:
- AI accelerates the identification of lead compounds, prediction of toxicity, and optimization of pharmacological properties.
- Computational methods and drug repurposing reduce development costs and risks.
- Genomic approaches enable tailored treatments for enhanced efficacy and reduced toxicity.
Conclusions:
- Emerging technologies like AI and genomics are transforming pharmaceutical development, leading to more targeted, safe, and effective drugs.
- These innovations are crucial for reducing trial costs and failure rates in the drug discovery pipeline.
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