For training and validation of DeepCocrystal, and the (retrained) benchmarks (i.e., Fingerprint-DNN, and Descriptor-DNN), the internal dataset was randomly split into a training (80%), a validation (10%), and a test (10%) set via stratified splitting (which preserves the original class frequency in each split). The procedure was repeated ten times with different random seeds to monitor the performance robustness. Every API-coformer SMILES pair is presented to the models twice, once per each network channel, to artificially inflate the training dataset as previously suggested .