DeepCocrystal was benchmarked with four existing approaches: (i) CCGNet , which relies on graph neural networks to perform a prediction; (ii) CC-Descriptor ML, which relies on an array of 'classical' machine learning models trained on co-crystal descriptors ; (iii) Descriptor-DNN, based on a fully-connected neural network trained on molecular descriptors ; and (iv) Fingerprint-DNN, a fullyconnected neural network trained on extended connectivity fingerprints . To ensure comparability and account for the lack of provided code, data, and/or hyperparameters, we re-implemented and trained Descriptor-DNN and Fingerprint-DNN, using the same dataset as DeepCocrystal (see Materials and Methods). These models were benchmarked for their performance on the internal and external test sets, whenever possible. CCGNet and CC-Descriptor-ML were excluded from the internal evaluation, due to data overlap between the training and test sets (CCGNet), and the extensive time required to calculate descriptors for thousands of data points (CC-Descriptor-ML).