title: LigSeeSVM: ligand-based virtual screening using support vector machines and data fusion.
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.
authors.affiliation: Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan. yanfu.bi93g@nctu.edu.tw
authors.forename: Yen-Fu
authors.initials: YF
authors.lastname: Chen
authors.forename: Kai-Cheng
authors.initials: KC
authors.lastname: Hsu
authors.forename: Po-Tsun
authors.initials: PT
authors.lastname: Lin
authors.forename: D Frank
authors.initials: DF
authors.lastname: Hsu
authors.forename: Bruce S
authors.initials: BS
authors.lastname: Kristal
authors.forename: Jinn-Moon
authors.initials: JM
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D012372:ROC Curve; D011960:Receptors, Estrogen / Q000819:agonists / Q000037:antagonists & inhibitors; D013937:Thymidine Kinase / Q000378:metabolism
chemical_list: D011960:Receptors, Estrogen; D013937:Thymidine Kinase