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Application of Machine Leaning in Drug Discovery and Development

Application of Machine Leaning in Drug Discovery and Development

Shuxing Zhang
ISBN13: 9781615209118|ISBN10: 1615209115|EISBN13: 9781615209125
DOI: 10.4018/978-1-61520-911-8.ch012
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MLA

Zhang, Shuxing. "Application of Machine Leaning in Drug Discovery and Development." Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, edited by Huma Lodhi and Yoshihiro Yamanishi, IGI Global, 2011, pp. 235-256. https://doi.org/10.4018/978-1-61520-911-8.ch012

APA

Zhang, S. (2011). Application of Machine Leaning in Drug Discovery and Development. In H. Lodhi & Y. Yamanishi (Eds.), Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques (pp. 235-256). IGI Global. https://doi.org/10.4018/978-1-61520-911-8.ch012

Chicago

Zhang, Shuxing. "Application of Machine Leaning in Drug Discovery and Development." In Chemoinformatics and Advanced Machine Learning Perspectives: Complex Computational Methods and Collaborative Techniques, edited by Huma Lodhi and Yoshihiro Yamanishi, 235-256. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61520-911-8.ch012

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Abstract

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.

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