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.
970 million druglike small molecules for virtual screening in the chemical universe database gdb-13
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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.