Rational High-Throughput Screening of Cocrystals: An Explainable Deep Learning Framework Based on Morgan Fingerprints and Cross-Attention.

Aiming to address the problems of high blindness in traditional screening methods, insufficient granularity of feature representation, and poor interpretability of existing machine learning prediction models in the field of cocrystal research and development, we propose a cocrystal prediction method based on Morgan fingerprints and the cross-attention mechanism. First, a high-quality cocrystal data set is constructed, and Morgan fingerprints are employed to characterize molecular structures. Subsequently, a fingerprint-driven cross-attention model is developed to capture the feature interaction correlations between molecules, thereby realizing end-to-end prediction of cocrystal formation propensity. Finally, the comprehensive performance of the model and the rationality of its prediction logic are systematically validated through multidimensional performance evaluation, horizontal comparison with mainstream models, hyperparameter sensitivity analysis, ablation experiments, and SHAP-based interpretability analysis. Experimental results show that the model achieves an AUC of 0.9878 on the independent test set, and its BACC and specificity are significantly superior to those of traditional machine learning and basic deep learning models. It effectively mitigates the issue of high false positive rate induced by class-imbalanced data sets. The prediction outcomes of the model can be traced back to specific structural sites of molecules via the SHAP method, which provides clear chemical guidance. This work can provide reliable technical support for the high-throughput rational screening of cocrystals in pharmaceuticals, functional materials, and related fields.