AI-DRIVEN PRECISION MEDICINE IN NEUROPHARMACOLOGY: REVOLUTIONIZING TREATMENT AND GUIDANCE FOR NEUROLOGICAL DISORDERS

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Published: Jun 30, 2026
DOI: https://doi.org/10.69857/joapt.v2i1.38
Keywords:
Artificial intelligence precision medicine neuropharmacology machine learning neurological disorders drug discovery biomarkers genomics

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Shreyansh Saha
Department of Pharmacology, Radiant Institute of Pharmaceutical Sciences, Kishanganj, Bihar-855107, India
Monideepa Dey
Department of Pharmaceutics, NETES Institute of Pharmaceutical Science, Kamrup, Assam-781125, India
Garima Kumari
Department of Pharmaceutics, Mata Gujri College of Pharmacy, Kishanganj, Bihar-855107, India
Khyati Mishra
Department of Pharmaceutics, NETES Institute of Pharmaceutical Science, Kamrup, Assam-781125, India
Sushmita Ghose
Department of Biotechnology, Gauhati University, Guwahati, Assam-781014, India
Manoj Kumar Deka
Department of Pharmaceutical Sciences, Nagaon University, Nagaon, Assam- 782001, India

Abstract

Background: Neurological disorders represent one of the most pressing and complex challenges in modern medicine, affecting hundreds of millions of individuals globally and imposing an enormous burden on healthcare systems. Traditional pharmacological approaches, while effective for some patients, are often limited by inter-individual variability in drug response, adverse effects, and the intricate pathophysiology underlying conditions such as Alzheimer’s disease, Parkinson’s disease, epilepsy, multiple sclerosis, and major depressive disorders. The emergence of artificial intelligence (AI) and machine learning (ML) as transformative tools in biomedical research has opened unprecedented avenues for precision medicines in neuropharmacology. This paper reviews the current landscape of AI-driven approaches for identifying neurological drug targets, predicting therapeutic outcomes, optimizing treatment regimens, and discovering novel neuroactive compounds. We further discuss the integration of multi-omics data, neuroimaging biomarkers, and electronic health records (EHRs) within AI frameworks, highlighting both the promising advances and the formidable challenges-including data privacy, algorithmic bias, and regulatory considerations- that must be addressed to realize the full clinical potential of AI-driven precision neuropharmacology. Objectives: This comprehensive review systematically examines the current landscape, principal methodologies, clinical applications, and translational challenges of AI-driven precision medicine in neuropharmacology, with a focus on Alzheimer’s disease (AD), Parkinson’s disease (PD), epilepsy, multiple sclerosis (MS), stroke, psychiatric disorders, and rare neurological conditions. Results and conclusions: AI methodologies- including supervised and unsupervised learning, deep learning architectures, graph neural networks, generative models, reinforcement learning, natural language processing, and federated learning- are delivering transformative advances across drug target identification, de novo molecular design, virtual screening, drug repurposing, neuroimaging biomarker analysis, and pharmacogenomics.

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Saha, S. ., Dey, M. ., Kumari, G. ., Mishra, K. ., Ghose, S. ., & Deka, M. K. . (2026). AI-DRIVEN PRECISION MEDICINE IN NEUROPHARMACOLOGY: REVOLUTIONIZING TREATMENT AND GUIDANCE FOR NEUROLOGICAL DISORDERS . Journal of Applied Pharmacology and Toxicology, 2(1), 01–16. https://doi.org/10.69857/joapt.v2i1.38
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Vol. 2 No. 1 (2026)
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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