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Support-vector-machine-based ranking significantly improves the effectiveness of similarity searching using 2D fingerprints and multiple reference compounds

: Geppert, H.; Horváth, T.; Gärtner, T.; Wrobel, S.; Bajorath, J.


Journal of chemical information and modeling 48 (2008), Nr.4, S.742-746
ISSN: 1549-9596
ISSN: 0095-2338
EISSN: 1520-5142
Fraunhofer IAIS ()

Similarity searching using molecular fingerprints is computationally efficient and a surprisingly effective virtual screening tool. In this study, we have compared ranking methods for similarity searching using multiple active reference molecules. Different 2D fingerprints were used as search tools and also as descriptors for a support vector machine (SVM) algorithm. In systematic database search calculations, a SVM-based ranking scheme consistently outperformed nearest neighbor and centroid approaches, regardless of the fingerprints that were tested, even if only very small training sets were used for SVM learning. The superiority of SVM-based ranking over conventional fingerprint methods is ascribed to the fact that SVM makes use of information about database molecules, in addition to known active compounds, during the learning phase.