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Molecule Kernels: A Descriptor- and Alignment-Free QSAR Approach
Citation key Mohr2008a
Author Mohr, J. and Jain, B. and Obermayer, K.
Pages 1868 – 1881
Year 2008
DOI 10.1021/ci800144y
Journal Journal of Chemical Information and Modeling
Volume 48
Number 9
Publisher American Chemical Society
Abstract Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial pre-alignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets, and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.
Bibtex Type of Publication Selected:applications
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