AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey

Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.

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Publication Details

Journal
IEEE Transactions on Computational Biology and Bioinformatics
Published
2024-11-06
DOI
https://doi.org/10.1109/tcbb.2024.3492708
Citations
12
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
4.34

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article

AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey

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12 citations
IEEE Transactions on Computational Biology and Bioinformatics
Computational Drug Discovery Methods
4.34
article

AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey

Cristian Minoccheri, Gilbert S. Omenn, Emily Wittrup, Kayvan Najarian, Flora Rajaei, Richard Wilson
article en
12 citations

Abstract

Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.

IEEE Transactions on Computational Biology and BioinformaticsVol. 22(1)
University of Michigan (US), University of York (GB)
National Science Foundation, National Institutes of Health, Michigan Institute for Data Science, University of Michigan
Openalex Percentile: Top 5%
Computational Drug Discovery Methods
4.34
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