Opportunities and Challenges Artificial Intelligence in Pharmacology : Artificial Intelligence in Pharmacology

This study explores the advancements in artificial intelligence (AI) and machine learning (ML) applications in pharmacology, emphasizing their role in enhancing drug discovery, development, and personalized treatment plans. The most significant achievement of this research is the demonstration of how AI techniques, particularly deep learning models, can effectively analyze complex biological data to predict drug interactions and optimize therapeutic outcomes. By integrating AI with traditional pharmacological methods, this study highlights the potential for reducing clinical trial failures and improving patient outcomes. Additionally, the research underscores the necessity for regulatory initiatives to promote data sharing, facilitating the development of robust AI models in pharmacology. Overall, this work contributes valuable insights into the transformative impact of AI on the future of drug development and clinical practice.

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Published
2024-10-09
DOI
https://doi.org/10.1109/ist64061.2024.10843462
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Opportunities and Challenges Artificial Intelligence in Pharmacology : Artificial Intelligence in Pharmacology

Omid Mehrpour, Jafar Abdollahi
Computational Drug Discovery Methods
article

Opportunities and Challenges Artificial Intelligence in Pharmacology : Artificial Intelligence in Pharmacology

Omid Mehrpour, Jafar Abdollahi
article en

Abstract

This study explores the advancements in artificial intelligence (AI) and machine learning (ML) applications in pharmacology, emphasizing their role in enhancing drug discovery, development, and personalized treatment plans. The most significant achievement of this research is the demonstration of how AI techniques, particularly deep learning models, can effectively analyze complex biological data to predict drug interactions and optimize therapeutic outcomes. By integrating AI with traditional pharmacological methods, this study highlights the potential for reducing clinical trial failures and improving patient outcomes. Additionally, the research underscores the necessity for regulatory initiatives to promote data sharing, facilitating the development of robust AI models in pharmacology. Overall, this work contributes valuable insights into the transformative impact of AI on the future of drug development and clinical practice.

Wayne State University (US), Islamic Azad University Central Tehran Branch (IR)
Openalex Percentile: Top 70%
Computational Drug Discovery Methods
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