Combines D-optimality and diversity-maximizing selection in an epsilon-greedy loop to create compact training sets for sparse group additivity and kernel ridge regression models of molecular properties.
Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
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Designing compact training sets for data-driven molecular property prediction
Combines D-optimality and diversity-maximizing selection in an epsilon-greedy loop to create compact training sets for sparse group additivity and kernel ridge regression models of molecular properties.