Model performance was evaluated with the MCC, precision, recall, AUROC and TOP-2 metrics. The MCC ranges from -1 to +1 and is a balanced measure for evaluating binary classifiers on unbalanced data sets. Recall quantifies the proportion of true positive samples recovered by the model, precision quantifies the proportion of true positives among predicted positives, AUROC describes the model's ability to rank positive samples above negative samples, and top-2 success rate quantifies the proportion of molecules with at least one known SOM listed among the two top-ranked atom positions. The compounds shown, from top to bottom, are as follows: daidzein, a naturally occurring isoflavone found in some legumes; S-allyl-L-cysteine, a bioactive compound from aged garlic extract used for its antioxidant and anti-inflammatory properties; prazosin, an alpha-blocker used to treat hypertension; fenbufen, a nonsteroidal anti-inflammatory drug with analgesic and antipyretic effects; gemcitabine, a nucleoside metabolic inhibitor employed as an adjunct therapy for certain cancers; and motesanib, a drug candidate formerly investigated for the treatment of cancer.