Artificial Intelligence-enabled cheminformatics approaches in Ayurveda-based drug discovery

ABSTRACT: Ayurveda contains a vast and diverse collection of botanicals, minerals, and classical formulations. Each Ayurvedic medicine often includes multiple components that act together on different biological pathways. This holistic approach naturally fits with modern network biology and systems pharmacology, which also study health as an outcome of interacting systems rather than isolated targets. Modern tools such as cheminformatics and Artificial Intelligence (AI) now make it possible to explore this complexity in a structured way. Using phytochemical databases, Quantitative Structure–Activity Relationship (QSAR) modeling, deep-learning-based Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction, and network pharmacology, researchers can analyze how Ayurvedic compounds interact at molecular and cellular levels. These technologies accelerate hypothesis generation, lead selection, and safety evaluation – while still respecting the core Ayurvedic principles of Dravyaguṇa (~pharmacognosy and pharmacology) and the quality standards of the Ayurvedic Pharmacopoeia of India (API). A stepwise integrative framework including Authenticating raw materials through API and/or Ayurvedic Formulary of India (AFI) monographs, Integrating data from Indian and global resources such as Indian Medicinal Plants, Phytochemistry And Therapeutics (IMPAT), ChEMBL, and PubChem, Screening compounds using QSAR, docking, and ADMET tools validated by OECD standards, Applying network pharmacology to understand polyherbal synergy, and Ensuring safety through AI-based toxicity prediction and pharmacovigilance aligned with ICMR ethics and the Ayushman Bharat Digital Mission digital ecosystem links classical Ayurvedic wisdom with modern digital science, creating a responsible, reproducible, and globally relevant model for Ayurveda-centric drug discovery.