Electronic supplementary material accompanies this paper and is available through the journal website. It provides the SMILES of the virtual compound library, SAscores of the 15 building blocks, and details of the computational and experimental data underlying the figures and tables throughout this paper and that were used in the statistical analysis. We also provide the list of descriptors used to develop the DNN model and the trained model in scikit-learn's pickle (.pkl) format. We note that this and other trained density models as well as the corresponding ML workflows are available as part of the ChemML package's template and model collection (e.g., for retraining, customization, or transfer learning) . Finally, we give detailed definitions of all statistical metrics employed in this work. paper is part of MAFA's PhD thesis . MH gratefully acknowledges support by Phase-I and Phase-II Software Fellowships (grant No. ACI-1547580-479590) of the NSF Molecular Sciences Software Institute (grant No. ACI-1547580) at Virginia Tech . We are grateful to Prof. Chong Cheng for valuable discussions and insights.