title: Predicting multiple binding modes using a kernel method based on a vector space model molecular descriptor.
abstract: We describe the use of our Vector Space Model Molecular Descriptor (VSMMD), based on a Vector Space Model (VSM) that is suitable for kernel studies in Quantitative Structure-Activity Relationship (QSAR) modelling. Our experiments provide convincing comparative empirical evidence that this kernel method can provide sufficient discrimination to predict various biological activities of a molecule with reasonable accuracy. Furthermore, together with a kernel feature space algorithm, experiments also provide convincing empirical evidence that our VSMMD can provide sufficient information to identify different binding modes with high accuracy.
authors.affiliation: The David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada. fjburkow@plg.uwaterloo.ca
authors.forename: Forbes J
authors.initials: FJ
authors.lastname: Burkowski
authors.forename: William W L
authors.initials: WW
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D016208:Databases, Factual; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000706:statistics & numerical data; D008024:Ligands; D008954:Models, Biological; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D014584:User-Computer Interface
chemical_list: D008024:Ligands