Models were evaluated in terms of Recall (ability to correctly classify positive pairs, Eq. 1), Precision (accuracy of positive predictions, Eq. 2), Specificity (ability to correctly classify negative pairs, Eq. 3) and Balanced Accuracy (overall performance, Eq. 4). These metrics were computed by considering predictions lower than 0.5 as a "negative", or "positive" otherwise (Table On the more challenging external set, the SMILES augmentation increased the Balanced Accuracy by 10% (or more) compared to using canonical SMILES strings. This aspect indicates a higher generalization potential provided by learning from different SMILES versions of the same molecule. When comparing different augmentation levels, DeepCocrystal (2:7 augmentation) shows a better performance than SMI-CNN(1:4), in terms of global performance (9% higher Balanced Accuracy) and minimization of false positives (14 % higher Precision) -indicating a greater potential for prospective applications.