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Predicting multiple binding modes using a kernel method based on a vector space model molecular descriptor.

作者信息

Burkowski Forbes J, Wong William W L

机构信息

The David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada.

出版信息

Int J Comput Biol Drug Des. 2009;2(1):58-80. doi: 10.1504/ijcbdd.2009.027584.

Abstract

We describe the use of our Vector Space Model Molecular Descriptor (VSMMD), based on a Vector Space Model (VSM) that is suitable for kernel studies in Quantitative Structure-Activity Relationship (QSAR) modelling. Our experiments provide convincing comparative empirical evidence that this kernel method can provide sufficient discrimination to predict various biological activities of a molecule with reasonable accuracy. Furthermore, together with a kernel feature space algorithm, experiments also provide convincing empirical evidence that our VSMMD can provide sufficient information to identify different binding modes with high accuracy.

摘要

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