Integrating Evolutionary and Compositional Features with ML and DL for Robust and Interpretable Druggable Protein Prediction

Druggable proteins are essential targets in contemporary therapeutic discovery, and their precise identification is necessary for the progression of rational drug design. Traditional biochemical screening techniques are costly, laborious, and protracted, whereas current computational models typically attain only moderate efficacy and seldom offer robust statistical validation or biological interpretability. To mitigate these limitations, we offer a hybrid computational approach that amalgamates evolutionary and compositional information by merging Average Block-based Position-Specific Scoring Matrix (AB-PSSM) characteristics with Dipeptide Composition (DPC) into a 600-dimensional representation. The hybrid feature space was assessed utilizing three machine learning algorithms (support vector machine, random forest, and XGBoost) alongside three deep learning architectures (CapsBiLSTM, ResCapsNetPlus, and ResNet1D) following a rigorous five-fold out-of-fold cross-validation protocol to guarantee fairness and reproducibility. A thorough assessment revealed that the hybrid attributes significantly surpassed individual descriptors, with the SVM and CapsBiLSTM classifiers attaining classification accuracies of 90% and ROC-AUC and PR-AUC values surpassing 95%. The robustness and reliability were further validated by statistical analyses, including DeLong’s test, McNemar’s test, and bootstrap confidence intervals, demonstrating that the observed enhancements were substantial and consistent. Furthermore, interpretability investigations employing SHAP feature attribution and t-SNE visualization underscored biologically significant sequence descriptors as pivotal factors influencing druggability, thus mitigating the "black box" constraint inherent in numerous deep learning methodologies. This study presents a statistically validated, interpretable, and high-performing framework for predicting druggable proteins, which outperforms current machine learning and ensemble-based methods, establishing a clear basis for future applications in precision medicine and drug discovery.