models/ginconv.py, gat.py, gat_gcn.py, and gcn.py: proposed models GINConvNet, GATNet, GAT_GCN, and GCNNet receiving graphs as input for drugs. Step-by-step running: 0.

2024年8月26日 · MvGraphDTA was a novel deep learning method based on graph convolutional networks for predicting DTA. It integrated multi-view features of graphs and line graphs from drugs and targets, and employed data augmentation to achieve superior performance compared to competitive state-of-the-art methods across multiple datasets.

2025年1月20日 · The MMPD-DTA model integrates graph and sequence modalities of targets, pockets, and drugs to capture both global and local target and drug information. The model introduces a novel pocket-drug graph (PD graph) that simultaneously models atomic interactions within the target, within the drug, and between the target and drug.

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.

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.

2023年9月30日 · Dynamic graph DTA (DGDTA), which uses a dynamic graph attention network combined with a bidirectional long short-term memory (Bi-LSTM) network to predict DTA is proposed in this paper. DGDTA adopts drug compound as input according to its corresponding simplified molecular input line entry system (SMILES) and protein amino acid sequence.

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. First, each drug is modeled as a graph, in which a vertex is an atom and an edge represents interaction between ...

2024年2月1日 · GTAMP-DTA combines special Attention mechanisms, assigning each atom or amino acid an attention vector. Interactions between drug forms and protein forms were considered to capture information about their interactions.

2020年6月1日 · In this paper, utilizing the structural information of molecules and proteins, two graphs of drug molecules and proteins are built up respectively. Graph neural networks are introduced to obtain their representations, and a method called DGraphDTA is …