Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network

Abstract

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multitask model for concurrent inhibition prediction of five major CYP450 isoforms, namely, 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training a multitask autoencoder deep neural network (DNN) on a large dataset containing more than 13 000 compounds, extracted from the PubChem BioAssay Database. We demonstrate that the multitask model gave better prediction results than that of single-task models, previous reported classifiers, and traditional machine learning methods on an average of five prediction tasks. Our multitask DNN model gave average prediction accuracies of 86.4% for the 10-fold cross-validation and 88.7% for the external test datasets. In addition, we built linear regression models to quantify how the other tasks contributed to the prediction difference of a given task between single-task and multitask models, and we explained under what conditions the multitask model will outperform the single-task model, which suggested how to use multitask DNN models more effectively. We applied sensitivity analysis to extract useful knowledge about CYP450 inhibition, which may shed light on the structural features of these isoforms and give hints about how to avoid side effects during drug development. Our models are freely available at http://repharma.pku.edu.cn/deepcyp/home.php or http://www.pkumdl.cn/deepcyp/home.php .

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity

“…Preliminary studies have shown that multi-task deep neural network (DNN) has better learning and adaptive ability compared to conventional machine learning approaches for drug discovery. 25–28 For instance, recently, Li and co-workers developed DNN models using multi-task deep autoencoder neural network for concurrent inhibition prediction of five major CYP450 isoforms. The predictive power of multi-task deep neural network outperformed other machine learning methods including logistic regression, support vector machine, C4.5 DT and k NN.…”

“…The predictive power of multi-task deep neural network outperformed other machine learning methods including logistic regression, support vector machine, C4.5 DT and k NN. 28…”

ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method

“…where TP , TN , FN , and TN are true positives, true negatives, false positives, and false negatives for the prediction of each label, respectively. These metrics have widely been used in a large number of bioinformatics applications recently (Feng et al, 2017; Niu and Zhang, 2017; Sun et al, 2017; Wang et al, 2017; Xu et al, 2017; He et al, 2018; Li et al, 2018; Pan et al, 2018; Qiao et al, 2018; Xiong et al, 2018; Xu et al, 2018; Zhang et al, 2018; Bian et al, 2019; Wei et al, 2019a; Wei et al, 2019b; Zou et al, 2019). In addition, we also calculated the area under the receive operating characteristic curve (AUC) by the trapezoidal rule.…”

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

“…Thus, it partitions label space and trains the base classifier to classify each subspace separately. Besides the aforementioned substrate prediction studies, other remarkable CYP450 inhibitor prediction studies have been published [136,[144][145][146]. Pang et al [145] collected data from BindingDB and ChEMBL and constructed a CYP450 3A4 isoform inhibitor prediction model.…”