DeepCocrystal was implemented in Tensorflow (v. 2.7.1). 2:7 positive:negative augmentation ratios were used for the training set. The SMILES strings were segmented into characters ('tokens'), padded to a length of 80, and fed into an embedding layer. Random search was used for hyperparameter optimization (Supporting Table ), by: (a) running 3000 hyper-parameter combinations, (b) using early stopping on validation accuracy, with a patience of five epochs, and a tolerance of 10 CC-Descriptors-ML. The model was re-implemented as described in the original paper . Each of the models of this approach (i.e., an ensemble of seven models developed using Support Vector Machine, XGBoost, Light Gradient-Boosting Machine, and Random Forest algorithms) was trained on 8, 10, or 14 selected features from 16 descriptors that show correlation with co-crystallization , including fingerprints, molecular radius, RDkit molecular descriptors and Hansen solubility parameters . Training set preparation followed the same procedure as before. The external test set performance was reported considering instances where at least four models produced positive results for co-crystals and negative results for non-co-crystals, as suggested by the authors. Duplicate entries between our external test set and the dataset shared for this model were checked and removed from the test set (five molecule pairs).