ArticleChemical science2022
MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 1 of them a synthesis that pooled it.
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Who cites it
106 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- KAN-GIN: Adaptive Nonlinear Molecular Representation Learning for Drug-Target Affinity Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- A Unified Hierarchical Multiscale Fusion Framework for Drug-Target Affinity Prediction: From Benchmark Performance to Nanomolar Inhibitor Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal deep learning with a joint uncertainty quantification scheme for drug-target interaction prediction.Molecular diversity · 2026Article
- MSCA-PLA: multi-scale cross-attention with differentiable pooling for protein-ligand binding affinity prediction.Molecular diversity · 2026Article
- A Length-Adaptive LLM-Enhanced Method for Drug-Target Interaction Prediction.International journal of molecular sciences · 2026Article
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- Bidirectional cross-modal fusion with tensor interaction for drug-target binding prediction.Scientific reports · 2026Article
- Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.Interdisciplinary sciences, computational life sciences · 2026Article
- DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity.BMC bioinformatics · 2026Article
- Structural Knowledge Is What Matters in Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- Relevance of 3D Rotationally Equivariant Neural Networks for Predicting Protein-Ligand Binding Affinities.Interdisciplinary sciences, computational life sciences · 2026Article
- Machine Learning Approaches for Compound-Target Interaction Prediction: A Review.Foods (Basel, Switzerland) · 2026Review
- Explainability Methods from Machine Learning Detect Important Drugs' Atoms in Drug-Target Interactions.Journal of chemical information and modeling · 2026Article
- Protein and ligand novelty in drug-target interaction prediction: a dual-encoder fusion strategy for more interpretable and generalizable modeling.BMC bioinformatics · 2026Article
- DecoderDTA: enhancing drug-target binding affinity prediction via decoding fidelity in multi-task learning.Journal of computer-aided molecular design · 2026Article
- LightDTA: lightweight drug-target affinity prediction via random-walk network embedding and knowledge distillation.Molecular diversity · 2026Article
- MFDR-DTI: Multisource Feature Decoupling and Dual-Level Reorganization Fusion for Accurate Drug-Target Interaction Prediction.ACS omega · 2026Article
- A novel method for drug-target affinity prediction by integrating predicted evolutionary information and multi-scale protein graphs.BMC biology · 2026Article
46 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
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Abstract
Predicting drug-target affinity (DTA) is beneficial for accelerating drug discovery. Graph neural networks (GNNs) have been widely used in DTA prediction. However, existing shallow GNNs are insufficient to capture the global structure of compounds. Besides, the interpretability of the graph-based DTA models highly relies on the graph attention mechanism, which can not reveal the global relationship between each atom of a molecule. 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. We also developed a novel visual explanation method, gradient-weighted affinity activation mapping (Grad-AAM), to analyze a deep learning model from the chemical perspective. We evaluated our approach using seven benchmark datasets and compared the proposed method to the state-of-the-art deep learning (DL) models. MGraphDTA outperforms other DL-based approaches significantly on various datasets. Moreover, we show that Grad-AAM creates explanations that are consistent with pharmacologists, which may help us gain chemical insights directly from data beyond human perception. These advantages demonstrate that the proposed method improves the generalization and interpretation capability of DTA prediction modeling.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.