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Computer-aided drug discovery is an essential component of modern drugdevelopment. Therein, deep learning has become an important tool for rapidscreening of billions of molecules in silico for potential hits containingdesired chemical features. Despite its importance, substantial challengespersist in training these models, such as severe class imbalance, high decisionthresholds, and lack of ground truth labels in some datasets. In this work weargue in favor of directly optimizing the receiver operating characteristic(ROC) in such cases, due to its robustness to class imbalance, its ability tocompromise over different decision thresholds, certain freedom to influence therelative weights in this compromise, fidelity to typical benchmarking measures,and equivalence to positive/unlabeled learning. We also propose new trainingschemes (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 thelogAUC metric that facilitates early enrichment (i.e. improves performance athigh decision thresholds, as often desired when synthesizing predicted hitcompounds). We demonstrate that these approaches outperform standard deeplearning approaches on a series of PubChem high-throughput screening datasetsthat represent realistic and diverse drug discovery campaigns on major drugtarget families.

Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions