Evaluation of machine-learning methods for ligand-based virtual screening.
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.
Chen
Beining
B
Department of Chemistry, University of Sheffield, Western Bank, Sheffield, UK.
Harrison
Robert F
RF
Papadatos
George
G
Willett
Peter
P
Wood
David J
DJ
Lewell
Xiao Qing
XQ
Greenidge
Paulette
P
Stiefl
Nikolaus
N
D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D015195:Drug Design; D008024:Ligands; D008962:Models, Theoretical
D008024:Ligands