Abstract
Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.
Keywords: qsar, rational drug design, docking, artificial intelligence, machine learning, pattern recognition, neural networks, support vector regression
Current Pharmaceutical Design
Title: Artificial Intelligence Approaches for Rational Drug Design and Discovery
Volume: 13 Issue: 14
Author(s): Wlodzislaw Duch, Karthikeyan Swaminathan and Jaroslaw Meller
Affiliation:
Keywords: qsar, rational drug design, docking, artificial intelligence, machine learning, pattern recognition, neural networks, support vector regression
Abstract: Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.
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Cite this article as:
Duch Wlodzislaw, Swaminathan Karthikeyan and Meller Jaroslaw, Artificial Intelligence Approaches for Rational Drug Design and Discovery, Current Pharmaceutical Design 2007; 13 (14) . https://dx.doi.org/10.2174/138161207780765954
DOI https://dx.doi.org/10.2174/138161207780765954 |
Print ISSN 1381-6128 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4286 |
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