Abstract

THE ROLE OF AI-DRIVEN PLATFORMS IN DRUG DISCOVERY: DATABASE INTEGRATION, APPLICATION, AND CASE STUDIES

A. Arun*, Dr. M. Ranjith, Dr. K. Rithika, S. Kavibharathi, M. Thirumeninathan

ABSTRACT

Artificial intelligence is rapidly reshaping drug research by providing methods to integrate and analyze broad biomedical datasets. With techniques like machine learning and natural language processing, AI platforms now enhance nearly every phase of drug design and development. High-quality databases covering chemical structures, biological responses, and clinical results are essential for building and validating these models. While major progress has been achieved—such as more efficient identification of new therapeutic candidates and prediction of molecular interactions—significant obstacles remain regarding data integration, quality, accessibility, and regulatory standards. Continued advances in AI, combined with reliable data and thorough experimental confirmation, are likely to expand the potentialof personalized therapies further and improve outcomes in precision medicine.

Keywords: Artificial Intelligence (AI), Machine Learning, Deep Learning, Natural Language Processing, Drug Development Pipeline, AI Techniques, Precision Medicine.