About GraphDTA: Predicting drug-target binding affinity with graph neural networks

2024年12月13日 · To address this issue, a dynamic heterogeneous graph prediction model, DynHeter-DTA, is proposed in this paper, which fully leverages the complex relationships between drug–drug, protein–protein, and drug–protein interactions, allowing the model to adaptively learn the optimal graph structures.

2025年1月20日 · DHAG-DTA introduces a two-level hierarchical graph structure: at the upper level, interactions between drug and target molecules are represented via an affinity graph and at the lower level, embedded molecular graphs represent …

2025年2月4日 · In this paper, we propose a new method, called GS-DTA, for predicting DTA based on graph and sequence models. GS-DTA takes simplified molecular input line input system (SMILES) of the drug and the protein amino acid sequence as input.

Inspired by GraphDTA, a method for predicting the affinity of drug-protein based on graph neural network is proposed, which is called DGraphDTA (double Graph DTA predictor). The method can predict the affinity only using the molecule SMILES and protein sequence.

2024年8月26日 · We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively.

2020年10月24日 · We propose a new model called GraphDTA that represents drugs as graphs and uses graph neural networks to predict drug–target affinity. We show that graph neural networks not only predict drug–target affinity better than non-deep learning models, but also outperform competing deep learning methods.

2024年11月1日 · We propose a new DTA prediction model that facilitates drug discovery research. We resolve issues from incomplete substructure learning and overlooked atoms. We solve insufficient sequence info and errors in predicted spatial features. We present a feature fusion tech, capturing their intricate interrelationships.

2022年10月1日 · We propose a novel hierarchical graph representation learning model for structure-free DTA prediction, named HGRL-DTA, to integrate coarse- and fine-level information in the hierarchical graph.

2022年1月5日 · In this study, we proposed a deep multiscale graph neural network based on chemical intuition for DTA prediction (MGraphDTA). We introduced a dense connection into the GNN and built a super-deep GNN with 27 graph convolutional layers to capture the local and global structure of the compound simultaneously.