A method for quantifying and visualizing the diversity of QSAR models
Feature selection is one of the most commonly used and reliable methods for deriving predictive quantitative structureactivity relationships (QSAR). Many feature selection algorithms are stochastic in nature and often produce different solutions depending on the initialization conditions. Because some features may be highly correlated, models that are based on different sets of descriptors may capture essentially the same information, however, such models are difficult to recognize. Here, we introduce a measure of similarity between QSAR models that captures the correlation between the underlying features. This measure can be used in conjunction with stochastic proximity embedding (SPE) or multi-dimensional scaling (MDS) to create a meaningful visual representation of structureactivity model space and aid in the post-processing and analysis of results of feature selection calculations.
feature selection quantitative structureactivity relationships stochastic proximity embedding multi-dimensional scaling nonlinear mapping point set similarity data mining