Objective Given the complexity of traditional Chinese medicine (TCM) formula constituents and their unclear interactions, a deep learning-based molecular interaction prediction model is constructed taking the prediction of interactions among constituents as the entry point, providing methodological support for identifying potential constituent interactions and subsequent studies on formula compatibility mechanisms. Methods We propose a molecule-molecule interaction prediction model based on molecular 3D structural features and Extended Connectivity Fingerprints, designated as 3D structure and extended connectivity fingerprints molecule-molecule interaction (3DFPMMI). The model employs the SchNet network to extract deep features from the 3D graph structure of molecules while simultaneously acquiring Extended Connectivity Fingerprints, and achieves effective fusion of multi-source features through a contrastive learning strategy. The model is trained on the DrugBank drug-drug interaction dataset, and cross&8209;domain generalization is implemented to realize the prediction of interactions among TCM constituents. Results The 3DFPMMI model achieves an accuracy of 0.921 and an area under the precision-recall curve of 0.972 on the DrugBank dataset, both outperforming other benchmark models. Ablation studies confirm that molecular 3D structural features, extended connectivity fingerprint features, and the contrastive learning-based feature fusion strategy are all core effective modules for improving model performance. When applied to predict component interactions in the classic TCM formulas Buyang Huanwu Decoction and Banxia Xiexin Decoction, some of the prediction results are highly consistent with the conclusions from existing experimental studies. Conclusion This study establishes a novel method for predicting interactions among TCM ingredients and preliminarily validates its application potential. This work offers new technical tools and methodological references for interpreting the compatibility mechanisms of TCM formulas at the molecular level.

traditional Chinese medicine components  /  molecular interaction  /  deep learning  /  cross-domain generalization  /  molecular structure  /  compatibility mechanism