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- DOI 10.18231/j.ijcaap.2025.013
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CrossMark
The role of artificial intelligence in advancing neuropharmacology
- Author Details:
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Pankaj Khuspe
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Sitaram Kale
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Nagnath Konapure
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Dipali Mane *
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Trushali Mandhare
Pankaj Khuspe
Sitaram Kale
Dipali Mane *
Trushali Mandhare
AI is rapidly transforming the field of neuropharmacology by introducing sophisticated computational methodologies capable of addressing the multifactorial complexity of central nervous system (CNS) disorders. By integrating ML, DL, and NLP, AI enables the analysis of high dimensional biomedical data, including genomics, proteomics, metabolomics, neuroimaging, and electronic health records. These capabilities support high throughput screening, accelerate de novo drug design, and enhance target identification by uncovering subtle biological correlations and mechanistic insights that often elude traditional experimental paradigms. AI based frameworks facilitate in silico prediction of pharmacokinetics and pharmacodynamics, drug–target interactions, and blood–brain barrier permeability. This empowers researchers to develop personalized therapeutic strategies for complex neurodegenerative and neuropsychiatric disorders such as Alzheimer’s disease, Parkinson’s disease, schizophrenia, and major depressive disorder. Several case studies underscore AI's translational potential: Benevolent AI’s application of NLP for drug repurposing in amyotrophic lateral sclerosis (ALS), Deep Genomics’ AI driven discovery of RNA targeted molecules, and Atomwise’s structure based compound optimization exemplify AI's impact across discovery and development pipelines. Despite its promise, the integration of AI into neuropharmacology is not without challenges. The opacity of deep learning models (“black box” problem), data heterogeneity, model generalizability, and evolving regulatory frameworks necessitate rigorous validation and interpretability efforts. This review comprehensively explores current applications, technological advancements, and future trajectories of AI in neuropharmacology. As the discipline evolves, AI stands poised as a foundational tool in the advancement of precision medicine, supporting more efficient, targeted, and individualized CNS pharmacotherapies.
Keywords: Artificial intelligence, Neuropharmacology, CNS drug discovery, Machine learning, Biomedical informatics.
References
- Puri V, Kanojia N, Sharma A, Huanbutta K, Dheer D, Sangnim T. Natural product-based pharmacological studies for neurological disorders. Front Pharmacol. 2022 Nov 7;13:1011740.
- Pinto-Coelho L. How Artificial intelligence is shaping medical imaging technology: A survey of innovations and applications. Bioengineering. 2023;10(12):1435.
- Ennab M, Mcheick H. Enhancing interpretability and accuracy of ai models in healthcare: A comprehensive review on challenges and future directions. Front Robot AI. 2024;11:1444763.
- Williamson SM, Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI Driven Healthcare. Appl Sci. 2024;14:675.
- Kumar P, Chaudhary B, Arya P, Chauhan R, Devi S, Parejiya PB, et al. Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research. Bioengineering. 2025;12(4):363.
- Dara S, Dhamercherla S, Jadav SS, Babu CM, Ahsan MJ. Machine learning in drug discovery: A review. Artif Intell Rev. 2022;55(3):1947–99.
- Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Front Public Health. 2023;11:1273253.
- Gu W, Yang X, Yang M, Han K, Pan W, Zhu Z. MarkerGenie: An NLP enabled text mining system for biomedical entity relation extraction. Bioinform Adv. 2022;2(1):vbac035.
- Zhang XM, Liang L, Liu L, Tang MJ. Graph neural networks and their current applications in bioinformatics. Front Genet. 2021;12:690049.
- Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou MM, et al. Artificial intelligence and machine learning aided drug discovery in central nervous system diseases: State of the arts and future directions. Med Res Rev. 2021;41(3):1427–73.
- Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial Intelligence in Drug Discovery and Development. Drug Discov Today. 2021;26(1):80–93.
- Batool M, Ahmad B, Choi S. A Structure Based Drug Discovery Paradigm. Int J Mol Sci. 2019;20(11):2783.
- Askr H, Elgeldawi E, Aboul Ella H, Elshaier YAM, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev. 2023;56(7):5975–
- Wu Y, Xie L. AI driven multi omics integration for multi scale predictive modeling of genotype environment phenotype relationships. Comput Struct Biotechnol J. 2025;27:265–77.
- Gao F, Huang K, Xing Y. Artificial Intelligence in Omics. Genomics Proteomics Bioinformatics. 2022;20(5):811–3.
- Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci. 2022;47(2):24.
- Sarkar C, Das B, Rawat VS, Wahlang JB, Nongpiur A, Tiewsoh I, et al. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. Int J Mol Sci. 2023;24:2026.
- Lai Y, Chu X, Di L, Gao W, Guo Y, Liu X, et al. Recent Advances in the Translation of Drug Metabolism and Pharmacokinetics Science for Drug Discovery and Development. Acta Pharm Sin B. 2022;12(6):2751–77.
- Aldoseri A, Al Khalifa KN, Hamouda AM. Re Thinking data strategy and integration for artificial intelligence: Concepts, Opportunities, and Challenges. Appl Sci. 2023;13:7082.
- Nabi AE, Pouladvand P, Liu L, Hua N, Ayubcha C. Machine learning in drug development for neurological diseases: A review of blood brain barrier permeability prediction models. Mol Inform. 2025;44(3):e202400325.
- Poweleit EA, Vinks AA, Mizuno T. Artificial intelligence and machine learning approaches to facilitate therapeutic drug management and model informed precision dosing. Ther Drug Monit. 2023;45(2):143–50.
- Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. 2021;14(1):86–93.
- Khan AF, Iturria Medina Y. Beyond the usual suspects: multi factorial computational models in the search for neurodegenerative disease mechanisms. Transl Psychiatry. 2024;14(1):386.
- Chamorey E, Gal J, Mograbi B, Milano G. Critical Appraisal and Future Challenges of Artificial Intelligence and Anticancer Drug Development. Pharmaceuticals. 2024;17(7):816.
- Wang Y, Lamim Ribeiro JM, Tiwary P. Machine Learning Approaches for Analyzing and Enhancing Molecular Dynamics Simulations. Curr Opin Struct Biol. 2020;61:139–145
- Cortial L, Montero V, Tourlet S, Del Bano J, Blin O. Artificial intelligence in drug repurposing for rare diseases: A mini review. Front Med. 2024;11:1404338.
- Kitaoka Y, Uchihashi T, Kawata S, Nishiura A, Yamamoto T, Hiraoka SI, et al. Role and potential of artificial intelligence in biomarker discovery and development of treatment strategies for amyotrophic lateral sclerosis. Int J Mol Sci. 2025;26(9):4346.
- Zarnack K, Eyras E. Artificial Intelligence and Machine Learning in RNA Biology. Brief Bioinform. 2023;24(6):bbad415.
- Ho D, Quake SR, McCabe ERB, Chng WJ, Chow EK, Ding X, et al. Enabling technologies for personalized and precision medicine. Trends Biotechnol. 2020;38(5):497–518.
- Chong PL, Vaigeshwari V, Mohammed Reyasudin BK, Noor Hidayah BRA, Tatchanaamoorti P, Yeow JA, et al. Integrating Artificial Intelligence in Healthcare: Applications, Challenges, and Future Directions. Future Sci OA. 2025;11(1):2527505.
- Han S, Wang M, Zhang J, Li D, Duan J. A review of large language models: fundamental architectures, key technological evolutions, interdisciplinary technologies integration, optimization and compression techniques, applications, and challenges. Electronics. 2024;13:5040. Khuspe et al. / IP International Journal of Comprehensive and Advanced Pharmacology 2025;10(2):87–93 93
- Parvin N, Joo SW, Jung JH, Mandal TK. Multimodal AI in biomedicine: pioneering the future of biomaterials, diagnostics, and personalized healthcare. Nanomaterials. 2025;15(12):895.
- Singh R, Paxton M, Auclair J. Regulating the AI enabled ecosystem for human therapeutics. Commun Med. 2025;5(1):181.
- García Reyes RA, Massó Quiñones LN, Ruy H, Castro DC. Neuropsychopharmacology in the era of artificial intelligence and biomolecule prediction software. NPP Digit Psychiatry Neurosci. 2025;3(1):16.
- Huang W, Shu N. AI powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders. Cell Rep Med. 2025;6(5):102132.
- Malheiro V, Santos B, Figueiras A, Mascarenhas Melo F. The potential of artificial intelligence in pharmaceutical innovation: From drug discovery to clinical trials. Pharmaceuticals. 2025;18(6):788.