To evaluate predictive performance, we applied several ML models, including eXtreme Gradient Boosting (XGBoost) , Random Forest (RF) , and Support Vector Classification (SVC) , using five-fold cross-validation to ensure robustness (Supplementary Fig. ). Iterative evaluations revealed that the XGBoost algorithm consistently outperformed other models in terms of cross-validation scores (0.92) and accuracy (1.0), establishing it as the foundation for subsequent analyses. The values of configurational hyperparameters are reported in the Supplementary Table . SHAP algorithm was employed to interpret the predictions of ML model for classifying polar and nonpolar cocrystals. The Python implementation of SHAP (version 0.46.0), available via PyPI or the conda-forge channel, was utilized for this analysis ( conda-forge/shap). SHAP leverages game theory principles and localized explanation techniques to provide precise, instance-specific insights into model predictions. These local explanations enable the identification of subtle relationships and small variations in the data that might be overlooked in generalized analyses. By examining SHAP values, we analyzed samples with significant variations, such as those distinguishing between polar and nonpolar cocrystals. SHAP explainers were built for XGBoost models trained on two datasets: one comprising 51 CA-based cocrystals retrieved from the CSD and another extended dataset that includes both these 51 cocrystals and 10 newly discovered structures identified through the approach illustrated in Fig. . This analysis provided critical insights into feature contributions to the overall model (Fig. and Supplementary Fig. ) and their influence on individual candidates (Fig. and Supplementary Fig. ) The three categories of features are distinguished by different colors, with the crystal-level features shown in olive green, the intermolecular-level features shown in blue and the molecular-level features shown in orange. In both the bar plot (Fig. and Supplementary Fig. ) and the beeswarm plot (Fig. and Supplementary Fig. ), the features are all listed in descending order of importance from top to bottom.