Decision tree models are basic tree-structured ML models. They can be easily understood and interpreted since their logical chain of arriving at a decision can be visualized. However, they su↵er from drawbacks, such as overfitting and high computation cost. Gradient boosting is a technique which ensembles numerous weak models (here, decision trees) to make a prediction. This ensemble of trees usually improves both prediction performance and lowers computation cost, however the comprehensibility, which a single decision tree o↵ers, is sacrificed. Extreme Gradient Boosting (XGBoost) is a ML algorithm (and the software library of the same name) which uses optimized gradient boosting, and is engineered to be highly e cient across di↵erent platforms and high dimension data. We prepared XGBoost binary classifiers as described below. Except for the scale pos weight hyperparameter, which was used to provide the weights of 'Non-Lysosomotropic' and 'Lysosomotropic' classes to control the class imbalance, default hyperparameters were used for training. The performances of the models trained with the default hyperparameters and with the optimized hyperparameters by the package Optuna 59 were found similar, and thereby the default hyperparameters were used. All the models were trained on the internal data. Stratified 5-fold cross-validation with the Balanced Accuracy and the Cohen's kappa score as model performance metrics, was used to validate the models. The libraries present in the scikit learn package were used for these calculations. Use of SHAP for Feature Importance Shapley Additive Explanations (SHAP) is a model-agnostic, game-theory based approach to explain ML models. SHAP focuses on local explanations, i. e., the impact of every input feature on the output of a single sample. This impact, a quantitative contribution of the feature, is called Shapley value (term used interchangeably with SHAP value) and is measured in log-odds. SHAP is increasingly used for model explanation in cheminformatics. We used the TreeExplainer from the SHAP Python package, since our models are tree-based. TreeExplainer has an important advantage over the default kernel SHAP. It computes exact Shapley values by taking advantage of the internal structure of the tree-based models with nominal computation power, whereas the default kernel SHAP uses an approximation of Shapley values to save the computation power it would need otherwise to calculate the exact values. Computing exact Shapley values allows global interpretation of the model by combining the local explanations. Due to the numerical nature of the descriptors, TreeExplainer could be used directly on the models trained on the molecular descriptors and various SHAP plots can be employed to study the descriptors and their importance on a data set. However, molecular fingerprints are binary in nature and especially in the case of Morgan fingerprints, multiple substructures can be encoded in the same bit. Thus, highlighting bits as important is unintuitive unless the substructures they encode are known. We used X-FP, a Python library, to compute the substructures of the bits which Morgan fingerprints encodes. We then used X-FP 's functionality to calculate feature importance by SHAP TreeExplainer and visualized these important bits and the substructures they encoded.