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Paper Title

Artificial Intelligence in Drug Discovery: Emerging Technologies, Explainable AI and Future Perspectives.

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Registration ID: IJNRD_327482

Published ID: IJNRD2607406

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Keywords

Artificial Intelligence, Drug Discovery, Machine Learning, Deep Learning, Generative AI, Explainable AI, ADMET, Precision Medicine

Abstract

Artificial Intelligence (AI) has revolutionized the drug discovery process by accelerating target identification, virtual screening, lead optimization, ADMET prediction, and clinical decision-making. Recent advances in Machine Learning (ML), Deep Learning (DL), Generative AI, and Explainable AI (XAI) have significantly improved the efficiency, accuracy, and cost-effectiveness of pharmaceutical research. This review discusses the role of AI across the drug discovery pipeline, recent technological advances, current challenges, ethical considerations, and future perspectives. AI has emerged as a powerful tool for modern pharmaceutical innovation; however, successful implementation requires high-quality data, rigorous validation, transparency, and regulatory acceptance. Artificial Intelligence (AI) has emerged as a transformative technology in modern drug discovery by improving the speed, accuracy, and efficiency of pharmaceutical research. Conventional drug discovery is a lengthy, expensive, and complex process with a high failure rate. AI-driven approaches, including machine learning, deep learning, natural language processing, and generative AI, have significantly enhanced various stages of the drug discovery pipeline, such as target identification, virtual screening, molecular docking, quantitative structure–activity relationship (QSAR) modeling, lead optimization, and ADMET prediction. These technologies enable researchers to analyze large biological datasets, identify potential drug candidates, and optimize compounds with improved precision while reducing both time and development costs. This review highlights the recent advancements, applications, challenges, and future perspectives of AI in drug discovery, with particular emphasis on explainable AI and emerging technologies. Despite challenges related to data quality, model interpretability, and regulatory acceptance, AI continues to reshape pharmaceutical research and accelerate the development of safer and more effective therapeutics. The integration of AI with human expertise is expected to play a pivotal role in the future of precision medicine and innovative drug development.

How To Cite (APA)

Atish Chopdar, Pratibha Adnaik, Aarati Varane, Rohit Nalwade, & Rushikesh Vadar (July-2026). Artificial Intelligence in Drug Discovery: Emerging Technologies, Explainable AI and Future Perspectives.. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(7), e75-e88. https://ijnrd.org/papers/IJNRD2607406.pdf

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Other Publication Details

Paper Reg. ID: IJNRD_327482

Published Paper Id: IJNRD2607406

Research Area: Pharmacy All

Author Type: Indian Author

Country: Kolhapur, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2607406.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2607406

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Publication Timeline

Paper Submission
18-07-2026
Peer Review
Through Scholar9.com Platform
Paper Acceptance
25-07-2026
Paper Publication
30-07-2026

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