Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.

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ID: 2964
2019
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Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
Reference Key
yang2019conceptschemical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yang, Xin;Wang, Yifei;Byrne, Ryan;Schneider, Gisbert;Yang, Shengyong;
Journal chemical reviews
Year 2019
DOI
10.1021/acs.chemrev.8b00728
URL
Keywords Keywords not found

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