Scope and Evolution of Artificial Intelligence in Pharmaceutical Sciences

Artificial Intelligence (AI) has become an integral component of pharmaceutical sciences, transforming drug discovery, development, and patient care. This review examines the historical context of AI in pharmaceuticals, its current applications, and prospects. The present explore the evolution from early rule-based systems to sophisticated machine-learning techniques that analyze vast datasets, enhancing efficiency and accuracy. Key applications include drug discovery, clinical trial optimization, personalized medicine, and drug manufacturing. However, the integration of AI poses challenges such as data privacy, bias, and regulatory compliance. By addressing these concerns and fostering collaboration among stakeholders, the pharmaceutical industry can harness the full potential of AI to improve patient outcomes and streamline processes. This review highlights AI's ongoing advancements and future directions in pharmaceutical sciences.

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Publication Details

Journal
Asian Journal of Applied Science and Technology
Published
2025-01-01
DOI
https://doi.org/10.38177/ajast.2025.9212
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
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Scope and Evolution of Artificial Intelligence in Pharmaceutical Sciences

Manish A. Kamble, Jagdish Baheti, Chetan S Darne, Pranita Mohod et al.
Asian Journal of Applied Science and Technology
Computational Drug Discovery Methods
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Scope and Evolution of Artificial Intelligence in Pharmaceutical Sciences

Manish A. Kamble, Jagdish Baheti, Chetan S Darne, Pranita Mohod, Shreya Pardhi
article en

Abstract

Artificial Intelligence (AI) has become an integral component of pharmaceutical sciences, transforming drug discovery, development, and patient care. This review examines the historical context of AI in pharmaceuticals, its current applications, and prospects. The present explore the evolution from early rule-based systems to sophisticated machine-learning techniques that analyze vast datasets, enhancing efficiency and accuracy. Key applications include drug discovery, clinical trial optimization, personalized medicine, and drug manufacturing. However, the integration of AI poses challenges such as data privacy, bias, and regulatory compliance. By addressing these concerns and fostering collaboration among stakeholders, the pharmaceutical industry can harness the full potential of AI to improve patient outcomes and streamline processes. This review highlights AI's ongoing advancements and future directions in pharmaceutical sciences.

Asian Journal of Applied Science and TechnologyVol. 09(02)
Openalex Percentile: Top 83%
Computational Drug Discovery Methods
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