Bcl∷ChemInfo - Qualitative analysis of machine learning models for activation of HSD involved in Alzheimer's Disease

In this case study, a ligand-based virtual high throughput screening suite, bcl::ChemInfo, was applied to screen for activation of the protein target 17-beta hydroxysteroid dehydrogenase type 10 (HSD) involved in Alzheimer's Disease. bcl::ChemInfo implements a diverse set of machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) with the extension for regression, kappa nearest neighbor (KNN), and decision trees (DT). Molecular structures were converted into a distinct collection of descriptor groups involving 2D- and 3D-autocorrelation, and radial distribution functions. A confirmatory high-throughput screening data set contained over 72,000 experimentally validated compounds, available through PubChem. Here, the systematical model development was achieved through optimization of feature sets and algorithmic parameters resulting in a theoretical enrichment of 11 (44% of maximal enrichment), and an area under the ROC curve (AUC) of 0.75 for the best performing machine learning technique on an independent data set. In addition, consensus combinations of all involved predictors were evaluated and achieved the best enrichment of 13 (50%), and AUC of 0.86. All models were computed in silico and represent a viable option in guiding the drug discovery process through virtual library screening and compound prioritization a priori to synthesis and biological testing. The best consensus predictor will be made accessible for the academic community at www.meilerlab.org.