文献类型：期刊文献

英文题名：Prediction of IDO1 Inhibitors by a Fingerprint-Based Stacking Ensemble Model Named IDO1Stack

作者：Sun, Huimin[1];Yang, Qing[2];Yu, Xinxin[1];Huang, Mengting[1];Ding, Meng[1];Li, Weihua[1];Tang, Yun[1];Liu, Guixia[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Fudan Univ, Sch Life Sci, State Key Lab Genet Engn, 2005 Songhu Rd, Shanghai 200438, Peoples R China

年份：2023

卷号：18

期号：17

外文期刊名：CHEMMEDCHEM

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001017631000001)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant 2019YFA0904800), the National Natural Science Foundation of China (Grants 82173746 and 82273858) and Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission, Grant 2021 Sci & Tech 03-28).

语种：英文

外文关键词：Tumor immunotherapy; IDO1 inhibitors; Machine learning; Deep learning; Stacking ensemble model

摘要：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.