LigSeeSVM: Ligand-based virtual Screening using Support Vector Machines and data fusion
by Yen-Fu Chen, Kai-Cheng Hsu, Po-Tsun Lin, D. Frank Hsu, Bruce S. Kristal, Jinn-Moon Yang
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 4, No. 3, 2011

Abstract: Ligand-based in silico drug screening is useful for lead discovery, in particular for those targets without structures. Here, we have developed LigSeeSVM, a ligand-based screening tool using data fusion and Support Vector Machines (SVMs). We used Atom Pair (AP) structure descriptors and Physicochemical (PC) descriptors of compounds to generate SVM-AP and SVM-PC models. Sequentially, the two models were combined using rank-based data fusion to create LigSeeSVM model. LigSeeSVM was evaluated on five data sets. Experimental results show that the performance of LigSeeSVM is better than other ligand-based virtual screening approaches. We believe that LigSeeSVM is useful for lead compounds.

Online publication date: Thu, 21-Jul-2011

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