AI-Driven Reverse Pharmacology & Accelerated Drug Repurposing Using Ancient Indian Manuscripts – A Comprehensive Review

Abstract

This paper presents a comprehensive literature review on AI-driven drug repurposing, contrasting traditional pharmacological methods with advanced AI-based approaches to elucidate their respective strengths and limitations. It systematically explores key computational techniques—such as network pharmacology, molecular docking, and deep learning—and their role in identifying drug-target interactions and predicting novel therapeutic indications. Special emphasis is placed on the digitalization and AI validation of traditional medical knowledge, particularly reverse pharmacology’s “bedside-to-bench” paradigm, which transforms historical clinical narratives into testable hypotheses. Through analysis of prominent case studies, the paper highlights both the transformative potential of AI integration in accelerating drug repurposing and the persistent challenges related to data heterogeneity and regulatory complexities. The vision of this review is to establish a holistic framework that synergizes computational innovations with ancestral wisdom, positioning AI-driven reverse pharmacology as a paradigm shift with the capacity to revolutionize drug discovery and therapeutic innovation. The mission is to provide critical insights and guide future research toward overcoming existing barriers, thereby enhancing the effectiveness and scope of medication repurposing strategies.