A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism. (November 2022)

• Record Type: Journal Article
• Authors: Qiu, Minyao; Liang, Xiaoqi; Deng, Siyao; Li, Yufang; Ke, Yanlan; Wang, Pingqing; Mei, Hu
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Cytochrome P450 -- Drug-drug interactions -- Graph convolutional neural network -- Attention mechanism -- Inhibitor; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106177
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Undesirable drug-drug interactions (DDIs) may lead to serious adverse side effects when more than two drugs are administered to a patient simultaneously. One of the most common DDIs is caused by unexpected inhibition of a specific human cytochrome P450 (CYP450), which plays a dominant role in the metabolism of the co-administered drugs. Therefore, a unified and reliable method for predicting the potential inhibitors of CYP450 family is extremely important in drug development. In this work, graph convolutional neural network (GCN) with attention mechanism and 1-D convolutional neural network (CNN) were used to extract the features of CYP ligands and the binding sites of CYP450 respectively, which were then combined to establish a unified GCN-CNN (GCNN) model for predicting the inhibitors of 5 dominant CYP isoforms, i.e., 1A2, 2C9, 2C19, 2D6, and 3A4. Overall, the established GCNN model showed good performances on the test samples and achieved better performances than the recently proposed iCYP-MFE model by using the same datasets. Based on the heat-map analysis of the resulting molecular graphs, the key structural determinants of the CYP inhibitors were further explored. Highlights: A unified GCNN model for predicting the inhibitors of the 5 CYP isoforms. Attention mechanism for extracting the structural determinants of CYP inhibitors. The GCNN model outperforms the state-of-art iCYP-MFE model.