title: Evaluation of machine-learning methods for ligand-based virtual screening.
abstract: Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.
authors.affiliation: Department of Chemistry, University of Sheffield, Western Bank, Sheffield, UK.
authors.forename: Beining
authors.initials: B
authors.lastname: Chen
authors.forename: Robert F
authors.initials: RF
authors.lastname: Harrison
authors.forename: George
authors.initials: G
authors.lastname: Papadatos
authors.forename: Peter
authors.initials: P
authors.lastname: Willett
authors.forename: David J
authors.initials: DJ
authors.lastname: Wood
authors.forename: Xiao Qing
authors.initials: XQ
authors.lastname: Lewell
authors.forename: Paulette
authors.initials: P
authors.lastname: Greenidge
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Stiefl
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D015195:Drug Design; D008024:Ligands; D008962:Models, Theoretical
chemical_list: D008024:Ligands