First report on machine learning based multiclass classification of Caco-2 permeability using different balancing strategies

Evaluating the permeability of different molecular structures across the Caco-2 cell line is crucial for drug discovery and development. The present study primarily focuses on developing machine learning-based multiclass classification models for predicting the permeability of molecules across the Caco-2 cell line. However, the class imbalance in permeability datasets poses a significant challenge for developing predictive models in the case of multiclass analysis. To address the class imbalance issue, we employed different balancing strategies, including oversampling, undersampling, and hybrid approaches, to balance the training set. A five-fold cross-validation approach was employed for optimizing the hyperparameters. After completion of the evaluation process, we concluded that the XGBoost multiclass classifier trained with ADASYN oversampling achieved the best performance (accuracy, 0.717; MCC, 0.512 on the test set). Additionally, extreme permeability classes were also classified separately, and the best model exhibited strong predictive performance (accuracy, 0.853; MCC, 0.703 on the test set). To enhance the interpretability of the best-performing models, we performed SHAP analysis to elucidate descriptor importance and provide explainability. Our findings demonstrate that appropriate data balancing strategies can significantly improve predictive performance in multiclass permeability classification, offering a valuable framework for drug permeability assessment.