Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

Abstract

Shape Signatures is a new computational tool that is being evaluated for applications in computational toxicology and drug discovery. The method employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule and then uses the output to construct compact histograms (i.e., signatures) that encode for molecular shape and polarity. In the present study, we extend the application of the Shape Signatures methodology to the domain of computational models for cardiotoxicity. The Shape Signatures method is used to generate molecular descriptors that are then utilized with widely used classification techniques such as k nearest neighbors ( k-NN), support vector machines (SVM), and Kohonen self-organizing maps (SOM). The performances of these approaches were assessed by applying them to a data set of compounds with varying affinity toward the 5-HT(2B) receptor as well as a set of human ether-a-go-go-related gene (hERG) potassium channel inhibitors. Our classification models for 5-HT(2B) represented the first attempt at global computational models for this receptor and exhibited average accuracies in the range of 73-83%. This level of performance is comparable to using commercially available molecular descriptors. The overall accuracy of the hERG Shape Signatures-SVM models was 69-73%, in line with other computational models published to date. Our data indicate that Shape Signatures descriptors can be used with SVM and Kohonen SOM and perform better in classification problems related to the analysis of highly clustered and heterogeneous property spaces. Such models may have utility for predicting the potential for cardiotoxicity in drug discovery mediated by the 5-HT(2B) receptor and hERG.