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). A confirmatory high-throughput screening data set contained over 72,000 experimentally validated compounds, available through PubChem (AID 886). 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 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% max), 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.

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