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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: March 2026
Volume 11 | Issue 3
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Impact Factor : 8.14
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| Paper Title: | AI in Computational Neuropharmacology: Treatment for Neurodegenerative Disorders. |
| Authors Name: | Mrs.P.S.Bhnadare , Mr. Omkar Patole , Ms. Sonali Deshmukh , Ms. Prajakta Sabale , Ms. Alfiya Patel |
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IJRTI_208625
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| Published Paper Id: | IJRTI2601102 |
| Published In: | Volume 11 Issue 1, January-2026 |
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| Abstract: | Neurodegenerative disorders remain among the most challenging diseases to treat due to their multifactorial pathophysiology and limited therapeutic success. The convergence of computational neuropharmacology and artificial intelligence (AI) presents an unprecedented opportunity to accelerate neurotherapeutic discovery. By combining systems-level models, quantitative pharmacology, and data-driven machine learning, researchers can simulate neural dynamics, predict drug–receptor interactions, and identify novel therapeutic targets. This review synthesizes current advancements in computational modeling, AI-based drug design, and systems neuropharmacology relevant to neurodegenerative disease research. It also discusses high-throughput neural mapping, network pharmacology, and predictive modeling as transformative tools for the development of next-generation neuropharmacological interventions. Finally, it outlines current limitations, ethical considerations, and emerging opportunities that define the future of AI-integrated computational neuropharmacology. |
| Keywords: | Neurodegenerative disorders remain among the most challenging diseases to treat due to their multifactorial pathophysiology and limited therapeutic success. The convergence of computational neuropharmacology and artificial intelligence (AI) presents an unprecedented opportunity to accelerate neurotherapeutic discovery. By combining systems-level models, quantitative pharmacology, and data-driven machine learning, researchers can simulate neural dynamics, predict drug–receptor interactions, and identify novel therapeutic targets. This review synthesizes current advancements in computational modeling, AI-based drug design, and systems neuropharmacology relevant to neurodegenerative disease research. It also discusses high-throughput neural mapping, network pharmacology, and predictive modeling as transformative tools for the development of next-generation neuropharmacological interventions. Finally, it outlines current limitations, ethical considerations, and emerging opportunities that define the future of AI-integrated computational neuropharmacology. |
| Cite Article: | "AI in Computational Neuropharmacology: Treatment for Neurodegenerative Disorders.", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.b7-b14, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601102.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2601102
Registration ID:208625
Published In: Volume 11 Issue 1, January-2026
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Page No: b7-b14 Country: Satara, Maharashtra, India. Research Area: Pharmacy Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601102 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601102 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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