Volume-2 ,Issue-9, September- 2026

Artificial intelligence (AI) and machine learning (ML) provide powerful computational tools for integrating heterogeneous biomedical data and identifying clinically relevant drug-disease and drug-target relationships. The present review focuses on an AI-driven drug repurposing methodology for selecting lead combinations of drugs for various diseases. The methodology encompasses disease characterization, integration of genomic and multi-omics data, identification of novel biomolecular targets, prediction of drug-target interactions, and prioritization of existing drugs for potential repurposing. Particular emphasis is placed on AI-based prediction of drug synergism and antagonism, where molecular targets, biological pathways, drug-response profiles, pharmacological characteristics, and drug-drug interaction data can be integrated to identify promising combinations. The application of AI in personalized medicine, pharmaceutical product development, clinical trial design, and combination drug delivery is also discussed. A systematic multi-parameter approach incorporating therapeutic potential, target complementarity, predicted synergy, safety, pharmacokinetic compatibility, and available clinical evidence can facilitate the selection of lead drug combinations for subsequent experimental validation. Despite its potential, challenges related to data quality, model interpretability, validation, bias, and clinical translation remain. Overall, AI-driven drug repurposing represents a promising framework for accelerating the rational discovery of effective drug combinations and supporting the development of precision and personalized therapeutic strategies for complex diseases.

Keyword: Drug Repurposing, Drug Development, Explainable Artificial Intelligence, Heart Failure, Drug Discovery