title: Development and Comparison of hERG Blocker Classifiers: Assessment on Different Datasets Yields Markedly Different Results.
abstract: In recent years, considerable effort has been invested in the development of classification models for prospective hERG inhibitors, due to the implications of hERG blockade for cardiotoxicity and the low throughput of functional hERG assays. We present novel approaches for binary classification which seek to separate strong inhibitors (IC50 <1 µM) from 'non-blockers' exhibiting moderate (1-10 µM) or weak (IC50 ≥10 µM) inhibition, as required by the pharmaceutical industry. Our approaches are based on (discretized) 2D descriptors, selected using Winnow, with additional models generated using Random Forest (RF) and Support Vector Machines (SVMs). We compare our models to those previously developed by Thai and Ecker and by Dubus et al. The purpose of this paper is twofold: 1. To propose that our approaches (with Matthews Correlation Coefficients from 0.40 to 0.87 on truly external test sets, when extrapolation beyond the applicability domain was not evident and sufficient quantities of data were available for training) are competitive with those currently proposed in the literature. 2. To highlight key issues associated with building and assessing truly predictive models, in particular the considerable variation in model performance when training and testing on different datasets.
authors.affiliation: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom.
authors.forename: Richard L
authors.initials: RL
authors.lastname: Marchese Robinson
authors.affiliation: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Glen
authors.affiliation: Biomedical Sciences Research Complex and EaStCHEM School of Chemistry, University of St Andrews, North Haugh, St Andrews, KY16 9ST, United Kingdom fax: (+) 44-1334 462595. jbom@st-andrews.ac.uk.
authors.forename: John B O
authors.initials: JB
authors.lastname: Mitchell
keywords: Classification; Toxicology; Virtual screening; Winnow; hERG