  Computer-aided drug discovery is an essential component of modern drug
development. Therein, deep learning has become an important tool for rapid
screening of billions of molecules in silico for potential hits containing
desired chemical features. Despite its importance, substantial challenges
persist in training these models, such as severe class imbalance, high decision
thresholds, and lack of ground truth labels in some datasets. In this work we
argue in favor of directly optimizing the receiver operating characteristic
(ROC) in such cases, due to its robustness to class imbalance, its ability to
compromise over different decision thresholds, certain freedom to influence the
relative weights in this compromise, fidelity to typical benchmarking measures,
and equivalence to positive/unlabeled learning. We also propose new training
schemes (coherent mini-batch arrangement, and usage of out-of-batch samples)
for cost functions based on the ROC, as well as a cost function based on the
logAUC metric that facilitates early enrichment (i.e. improves performance at
high decision thresholds, as often desired when synthesizing predicted hit
compounds). We demonstrate that these approaches outperform standard deep
learning approaches on a series of PubChem high-throughput screening datasets
that represent realistic and diverse drug discovery campaigns on major drug
target families.
