AutoQSAR

AutoQSAR democratizes creation and application of QSAR models through automation, following a best practices QSAR modeling workflow. With AutoQSAR, high-quality, predictive QSAR models can be created and employed with confidence. The best practices workflow includes descriptor generation, feature selection, creation of a large number of QSAR models from several methods including kernel-based partial least squares, naive bayes, and ensemble-based recursive partitioning with different training/test set splits, and ranking of QSAR models by performance. Predictions can be made from a consensus of the best models or from a particular model.