title: Virtual screening data fusion using both structure- and ligand-based methods.
abstract: Virtual screening is widely applied in drug discovery, and significant effort has been put into improving current methods. In this study, we have evaluated the performance of compound ranking in virtual screening using five different data fusion algorithms on a total of 16 data sets. The data were generated by docking, pharmacophore search, shape similarity, and electrostatic similarity, spanning both structure- and ligand-based methods. The algorithms used for data fusion were sum rank, rank vote, sum score, Pareto ranking, and parallel selection. None of the fusion methods require any prior knowledge or input other than the results from the single methods and, thus, are readily applicable. The results show that compound ranking using data fusion improves the performance and consistency of virtual screening compared to the single methods alone. The best performing data fusion algorithm was parallel selection, but both rank voting and Pareto ranking also have good performance.
authors.affiliation: Organic Pharmaceutical Chemistry, Department of Medicinal Chemistry, BMC, Uppsala University, P.O. Box 574, SE-751 23 Uppsala, Sweden.
authors.forename: Fredrik
authors.initials: F
authors.lastname: Svensson
authors.forename: Anders
authors.initials: A
authors.lastname: Karlén
authors.forename: Christian
authors.initials: C
authors.lastname: Sköld
mesh_terms: D000465:Algorithms; D001688:Biological Products / Q000737:chemistry* / Q000494:pharmacology; D003198:Computer Simulation; D017076:Computer-Aided Design; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D057166:High-Throughput Screening Assays; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D011506:Proteins / Q000819:agonists / Q000037:antagonists & inhibitors / Q000737:chemistry*; D012372:ROC Curve; D055672:Static Electricity; D014584:User-Computer Interface
chemical_list: D001688:Biological Products; D008024:Ligands; D011506:Proteins