Abstract

ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY AND DEVELOPMENT: A REVIEW

*Rutuja S. Barange, Chetan D. Shende, Dr. Nitin B. Kohale, Dr. H. S. Sawarkar

ABSTRACT

The integration of Artificial Intelligence (AI) into drug discovery and development is revolutionizing pharmaceutical research by accelerating the identification of potential drug candidates, optimizing clinical trials, and reducing costs. AI-driven techniques such as machine learning (ML), deep learning (DL), and natural language processing (NLP) are enhancing the efficiency of drug discovery by predicting molecular interactions, identifying novel drug targets, and personalizing treatment strategies. AI has demonstrated success in drug repurposing, de novo drug design, and biomarker identification, significantly reducing the time required for drug development. Despite these advancements, challenges such as data quality, regulatory concerns, and ethical considerations need to be addressed for the full-scale implementation of AI in drug development. This review provides an overview of AI applications in drug discovery, highlights recent advancements, and discusses challenges and future directions.

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