MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism,Journal of Chemical Information and Modeling

当前位置： X-MOL 学术 › J. Chem. Inf. Model. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-08-13 , DOI: 10.1021/acs.jcim.4c00957
Lihong Peng ₁ , Xin Liu ₁ , Min Chen ₂ , Wen Liao ₃ , Jiale Mao ₃ , Liqian Zhou ₁

Affiliation

Drug-Target Interaction (DTI) prediction facilitates acceleration of drug discovery and promotes drug repositioning. Most existing deep learning-based DTI prediction methods can better extract discriminative features for drugs and proteins, but they rarely consider multimodal features of drugs. Moreover, learning the interaction representations between drugs and targets needs further exploration. Here, we proposed a simple M ulti-modal G ating N etwork for DTI prediction, MGNDTI, based on multimodal representation learning and the gating mechanism. MGNDTI first learns the sequence representations of drugs and targets using different retentive networks. Next, it extracts molecular graph features of drugs through a graph convolutional network. Subsequently, it devises a multimodal gating network to obtain the joint representations of drugs and targets. Finally, it builds a fully connected network for computing the interaction probability. MGNDTI was benchmarked against seven state-of-the-art DTI prediction models (CPI-GNN, TransformerCPI, MolTrans, BACPI, CPGL, GIFDTI, and FOTF-CPI) using four data sets (i.e., Human, C. elegans, BioSNAP, and BindingDB) under four different experimental settings. Through evaluation with AUROC, AUPRC, accuracy, F1 score, and MCC, MGNDTI significantly outperformed the above seven methods. MGNDTI is a powerful tool for DTI prediction, showcasing its superior robustness and generalization ability on diverse data sets and different experimental settings. It is freely available at https://github.com/plhhnu/MGNDTI.

中文翻译：

MGNDTI：基于多模态表示学习和门控机制的药物-靶点相互作用预测框架

药物-靶标相互作用（DTI）预测有助于加速药物发现并促进药物重新定位。现有的大多数基于深度学习的DTI预测方法可以更好地提取药物和蛋白质的判别特征，但很少考虑药物的多模态特征。此外，学习药物和靶点之间的相互作用表示还需要进一步探索。在这里，我们提出了一种基于多模态表示学习和门控机制的简单的用于DTI预测的多模态门控网络 MGNDTI。 MGNDTI 首先使用不同的保持网络学习药物和靶标的序列表示。接下来，它通过图卷积网络提取药物的分子图特征。随后，它设计了一个多模式门控网络来获得药物和靶标的联合表示。最后，它构建了一个全连接网络来计算交互概率。 MGNDTI 使用四个数据集（即人类、线虫、BioSNAP、和 BindingDB）在四种不同的实验设置下。通过AUROC、AUPRC、准确率、F1分数和MCC的评估，MGNDTI显着优于上述七种方法。 MGNDTI 是 DTI 预测的强大工具，在不同的数据集和不同的实验设置上展示了其卓越的鲁棒性和泛化能力。它可以在 https://github.com/plhhnu/MGNDTI 上免费获取。

更新日期：2024-08-13

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南