Beyond the Scope of Free-Wilson Analysis. 2: Can Distance Encoded R-Group Fingerprints Provide Interpretable Nonlinear Models?

Abstract

In a recent study, we presented a novel quantitative-structure-activity-relationship (QSAR) approach, combining R-group signatures and nonlinear support-vector-machines (SVM), to build interpretable local models for congeneric compound sets. Here, we outline further refinements in the fingerprint scheme for the purpose of analyzing and visualizing structure-activity relationships (SAR). The concept of distance encoded R-group signature descriptors is introduced, and we explore the influence of different signature encoding schemes on both interpretability and predictive power of the SVM models using ten public data sets. The R-group and atomic gradients provide a way to interpret SVM models and enable detailed analysis of structure-activity relationships within substituent groups. We discuss applications of the method and show how it can be used to analyze nonadditive SAR and provide intuitive and powerful SAR visualizations.