Prediction of IDO1 Inhibitors by a Fingerprint‐Based Stacking Ensemble Model Named IDO1Stack

Abstract

Indoleamine 2,3‐dioxygenase 1 (IDO1) is viewed as an extremely promising target for cancer immunotherapy. Here, we proposed a two‐layer stacking ensemble model, IDO1Stack, that can efficiently predict IDO1 inhibitors. First, we constructed a series of classification models based on five machine learning algorithms and eight molecular characterization methods. Then, a stacking ensemble model was built using the top five models as the base classifier and logistic regression as the meta‐classifier. The areas under the receiver operating characteristic curve (AUC) of IDO1Stack on the test set and external validation set were 0.952 and 0.918, respectively. Furthermore, we computed the applicability domain and privileged substructures of the model and interpreted the model using SHapley Additive exPlanations (SHAP). It is expected that IDO1Stack can well study the interaction between target and ligand, providing practitioners with a reliable tool for rapid screening and discovery of IDO1 inhibitors.

Predicting depression in patients with heart failure based on a stacking model

“…By implementing stacking ensemble learning, we can leverage the strengths of individual models to enhance the predictive capability of the overall model. Although stacking integration strategies have been utilized in the medical field[ 40 - 42 ], there is a gap in research regarding the application of the stacking algorithm for identifying HF patients at high risk of depression. In our study, we selected DT, XGBoost and LightGBM as the first-level base learners, with Lasso serving as the second-level meta-learner.…”