iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding

Abstract

The human cytochrome P450 (CYP) superfamily holds responsibilities for the metabolism of both endogenous and exogenous compounds such as drugs, cellular metabolites, and toxins. The inhibition exerted on the CYP enzymes is closely associated with adverse drug reactions encompassing metabolic failures and induced side effects. In modern drug discovery, identification of potential CYP inhibitors is, therefore, highly essential. Alongside experimental approaches, numerous computational models have been proposed to address this biochemical issue. In this study, we introduce iCYP-MFE, a computational framework for virtual screening on CYP inhibitors toward 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms. iCYP-MFE contains a set of five robust, stable, and effective prediction models developed using multitask learning incorporated with molecular fingerprint-embedded features. The results show that multitask learning can remarkably leverage useful information from related tasks to promote global performance. Comparative analysis indicates that iCYP-MFE achieves three predominant tasks, one equivalent task, and one less effective task compared to state-of-the-art methods. The area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR) were two decisive metrics used for model evaluation. The prediction task for CYP2D6-inhibition achieves the highest AUC-ROC value of 0.93 while the prediction task for CYP1A2-inhibition obtains the highest AUC-PR value of 0.92. The substructural analysis preliminarily explains the nature of the CYP-inhibitory activity of compounds. An online web server for iCYP-MFE with a user-friendly interface was also deployed to support scientific communities in identifying CYP inhibitors.

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

“…Cytochrome P450 (CYP) is a superfamily of heme-containing oxidizing enzymes responsible for the metabolism of a wide variety of drugs, xenobiotics and endogenous molecules [ 1 – 4 ]. Five of the human CYPs (1A2, 2C9, 2C19, 2D6, and 3A4) are involved in ∼95% of the CYP-mediated metabolism of drugs representing ∼75% of drug metabolism [ 5 ].…”

eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning

“…While deep learning models need weight tuning at several penultimate layers only, classical machine learning models demand more computationally intensive tasks (feature selection, hyper-parameter tuning, etc.). Deep learning has now become one of the most robust computational approaches which is used to address diverse problems in molecular biology [32] , [33] , [34] , [35] , [36] , [37] , [38] and biochemistry [39] , [40] , [41] , [42] . Our prediction framework was designed to predict the MICs of multiple antibiotics against a typical strain of infectious bacteria only to demonstrate the computational and timing efficiency of the proposed method.…”

A multi-task FP-GNN framework enables accurate prediction of selective PARP inhibitors

“…For example, Cai et al (2019) established a multitask deep neural network (DNN) framework for comprehensive assessment of hERG blockers, which achieved higher predictive accuracy compared to other baseline models. In 2021, Nguyen-Vo et al (2021) developed iCYP-MFE, a computational framework for accurately predicting the inhibitory activity of molecules against five CYP isoforms (1A2, 2C9, 2C19, 2D6, and 3A4).…”