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BCL: : Conf: small molecule conformational sampling using a knowledge based rotamer library., , and . J. Cheminformatics, (2015)3D Deep Learning for Biological Function Prediction from Physical Fields., , , , , , , and . 3DV, page 928-937. IEEE, (2020)BCL: : Mol2D - a robust atom environment descriptor for QSAR modeling and lead optimization., , , and . J. Comput. Aided Mol. Des., 33 (5): 477-486 (2019)Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign., , and . J. Comput. Aided Mol. Des., 30 (3): 209-217 (2016)Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins., , , , , and . J. Chem. Inf. Model., 56 (2): 423-434 (2016)BCL: : MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping., , and . J. Chem. Inf. Model., 59 (2): 689-701 (2019)Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions., , , , , , , , and . CoRR, (2020)Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout., and . J. Comput. Aided Mol. Des., 30 (2): 177-189 (2016)General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps., , , and . J. Chem. Inf. Model., 61 (2): 603-620 (2021)BCL: : Conf: Improved Open-Source Knowledge-Based Conformation Sampling Using the Crystallography Open Database., , , and . J. Chem. Inf. Model., 61 (1): 189-201 (2021)