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Pankaj Kumar*,
Srishti Singh,
- Student, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi (IIT Delhi), Delhi, India
- Student, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi (IIT Delhi), Delhi, India
Abstract
Artificial intelligence (AI) is reshaping drug discovery by accelerating target identification, molecular design, structure prediction, docking, ADMET profiling, and clinical translation. Breakthroughs such as AlphaFold’s protein structure prediction have shortened key steps of the discovery pipeline, while diffusion/transformer-based generative models, ultra-large virtual screening, deep learning–based docking, and image-based phenotypic discovery are broadening the accessible chemical and biological space. Federated learning now enables cross-company modeling without sharing raw data, and early clinical signals—including AI-prioritized repurposing (baricitinib for COVID-19) and AI-designed molecules entering trials—illustrate translational potential. However, challenges remain in data quality, generalization, interpretability, prospective validation, and regulatory harmonization. We summarize core methods, representative successes, limitations, and emerging directions, and outline a pragmatic roadmap for impact and evaluation.
Keywords: Artificial intelligence (AI); Machine learning (ML); Drug discovery; Protein structure prediction; Molecular docking; Generative models.
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Research and Reviews : Journal of Computational Biology
| Volume | 15 | |
| 03 | ||
| Received | 27/10/2026 | |
| Accepted | 29/10/2026 | |
| Published | 10/11/2026 | |
| Publication Time | 14 Days |