Artificial Intelligence Approaches to Rational Drug Design and Discovery

Wlodzislaw Duch,
School of Computer Engineering, Nanyang Technological University, Singapore,
and Department of Informatics, Nicolaus Copernicus University,
Grudziadzka 5, 87-100 Torun, Poland.

Karthikeyan Swaminathan and Jaroslaw Meller,
Division of Biomedical Informatics, Children’s Hospital Research Foundation,
3333 Burnet Avenue, Cincinnati, OH 45242, USA


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.

Preprint for comments in PDF, 260 KB.
Reference: Current Pharmaceutical Design, 13(14), 1497-1508, 2007.

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