Synthon-CGR: Co-crystal Formation Prediction with Crystal-Supervised Synthon Reasoning

Co-crystallization offers a practical way to tune the physicochemical properties of molecular compounds without changing their covalent structure, but finding compatible molecular pairs still requires substantial experimental effort. Most machine-learning approaches treat this problem as binary classification and are trained only on pair-level formation labels. As a result, they provide limited information about the intermolecular motifs that support a predicted co-crystal. We introduce Synthon Candidate Graph Reasoner (Synthon-CGR), a graph neural network that predicts co-crystal formation together with a ranked set of candidate supramolecular synthons. The model combines molecular graph representations, functional motifs, and crystal-derived supervision so that intermolecular interactions become explicit prediction targets rather than post-hoc explanations. We curate literature and crystallographic data, augment the training pool to 27,718 molecular pairs to address the scarcity of reported negatives, and derive synthon labels from resolved crystal structures. On a pair-disjoint held-out test set, Synthon-CGR reaches F1 = 0.893 for co-crystal formation and HitRate@5 = 0.935 on the synthon-ranking subset. We also evaluate the model on an independent dataset obtained from co-crystallization experiments conducted in our laboratory and compare predicted interaction motifs with expert annotations for experimentally confirmed co-crystals. Together, these experiments show that crystal-derived interaction supervision can extend pair-level co-crystal prediction with explicit and experimentally testable hypotheses about molecular recognition.