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AI-Driven Pharmacology: Leveraging Machine Learning for Precision Medicine and Drug Discovery

by Shahadatul Islam, Sharmin Sultana Lincoln, Mysha Anjum Rupa
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 1
Year of Publication: 2025
Authors: Shahadatul Islam, Sharmin Sultana Lincoln, Mysha Anjum Rupa
10.5120/ijca2025924623

Shahadatul Islam, Sharmin Sultana Lincoln, Mysha Anjum Rupa . AI-Driven Pharmacology: Leveraging Machine Learning for Precision Medicine and Drug Discovery. International Journal of Computer Applications. 187, 1 ( May 2025), 15-24. DOI=10.5120/ijca2025924623

@article{ 10.5120/ijca2025924623,
author = { Shahadatul Islam, Sharmin Sultana Lincoln, Mysha Anjum Rupa },
title = { AI-Driven Pharmacology: Leveraging Machine Learning for Precision Medicine and Drug Discovery },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 1 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 15-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number1/ai-driven-pharmacology-leveraging-machine-learning-for-precision-medicine-and-drug-discovery/ },
doi = { 10.5120/ijca2025924623 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-17T02:45:23.259774+05:30
%A Shahadatul Islam
%A Sharmin Sultana Lincoln
%A Mysha Anjum Rupa
%T AI-Driven Pharmacology: Leveraging Machine Learning for Precision Medicine and Drug Discovery
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 1
%P 15-24
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Embracing the potential of AI-driven pharmacology, this study addresses the challenge of bridging toxicity screening and drug efficacy predictions by leveraging a multi-task deep learning framework tailored for personalized medicine. We integrated patient genomic data with extensive chemical descriptors, employing attention-based interpretability modules to enhance model transparency and systematically evaluate both adverse effects and binding affinity within a single network architecture. Experimental results on real-world patient records and a curated compound library revealed a 12% increase in classification accuracy over traditional baselines, a mean squared error of 0.18 in affinity predictions, and clear functional group insights explaining toxicity risks. These findings suggest that a unified approach to pharmacological modeling can not only expedite drug development but also improve patient-specific outcomes, with implications for streamlined research pipelines and more effective precision therapies.

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Index Terms

Computer Science
Information Sciences
Artificial Intelligence (AI)
Machine Learning
Deep Learning Pharmacology
Drug Discovery
Biomedical Research
Computational Biology Health Informatics
Precision Medicine
Data Science in Healthcare

Keywords

Multi-task deep learning toxicity classification binding affinity personalized medicine interpretability AI-driven pharmacology drug discovery

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