ABSTRACT
Partial and incremental stratification analysis of a quantitative structure-interference relationship (QSIR) is a novel strategy intended to categorize classification provided by machine learning techniques. It is based on a 2D mapping of classification statistics onto two categorical axes: the degree of consensus and level of applicability domain. An internal cross-validation set allows to determine the statistical performance of the ensemble at every 2D map stratum and hence to define isometric local performance regions with the aim of better hit ranking and selection. During training, isometric stratified ensembles (ISE) applies a recursive decorrelated variable selection and considers the cardinal ratio of classes to balance training sets and thus avoid bias due to possible class imbalance. To exemplify the interest of this strategy, three different highly imbalanced PubChem pairs of AmpC ß-lactamase and cruzain inhibition assay campaigns of colloidal aggregators and complementary aggregators data set available at the AGGREGATOR ADVISOR predictor web page were employed. Statistics obtained using this new strategy show outperforming results compared to former published tools, with and without a classical applicability domain. ISE performance on classifying colloidal aggregators shows from a global AUC of 0.82, when the whole test data set is considered, up to a maximum AUC of 0.88, when its highest confidence isometric stratum is retained.
Subject(s)
Algorithms , ConsensusABSTRACT
High-throughput screening has shown that Retro-1 inhibits ricin and Shiga toxins by diminishing their intracellular trafficking via the retrograde route, from early endosomes to the Golgi apparatus. To improve the activity of Retro-1, a structure-activity relationship (SAR) study was undertaken and yielded an analogue with a roughly 70-fold better half-maximal effective concentration (EC50) against Shiga toxin cytotoxicity measured in a cell protein synthesis assay.
