AL-ENABLED COMPUTATIONAL STRATEGIES IN HERBAL DRUG DISCOVERY: MOLECULAR DOCKING, NETWORK PHARMACOLOGY, AND PREDICTIVE MODELING

Harshini R. L.; Guna Kulothungan; T. M. Vijayalakshmi

doi:10.29121/jahim.v6.i1.2026.86

Authors

Harshini R. L. Department of Medical Biochemistry, Dr. Almpgibms University of Madras, Taramani Campus, Chennai, India
Guna Kulothungan Department of Biochemistry, Saveetha Medical College and Hospital, Saveetha University, SIMATS, Thandalam, Chennai, India
T. M. Vijayalakshmi Department of Medical Biochemistry, Dr. ALM Post Graduate Institute of Basic Medical Sciences, University of Madras, Taramani Campus, Chennai, India

DOI:

https://doi.org/10.29121/jahim.v6.i1.2026.86

Keywords:

Artificial Intelligence, Graph Neural Networks, Admet, Ethnopharmacology

Abstract [English]

The AI technology causes a big change that affects all the old ways of making herbal medicine. The evaluation study employs three scientific methodologies: molecular docking, network pharmacology, and predictive modeling, to ascertain the application of AI technology in scientific research. AI can fix structural gap problems thanks to the deep learning as a whole system and AlphaFold technology. This technology lets users look at large sets of phytochemical substance data. Systems that target multiple pathways along with show how herbal mixtures interact can help researchers find new drugs. Companies are changing ADMET research with AI-based prediction modeling because this method lets them find possible toxicity and metabolic syndrome. stability issues early in the product development process. The AI technologies link modern precision medicine methods with older ethnopharmacology methods by fixing problems with data standardization and "black-box" evaluation. The two groups work together to study the therapeutic uses of botanical materials, which helps them come up with new multi-component solutions for complicated health problems more quickly.

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