mlsanityData to 14 Oct 2026

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

arXiv 2107.10670 ↗First posted 21 Jul 2021q-bio.QM cs.LGcited by 60

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Abstract (arXiv)

Drug discovery often relies on the successful prediction of protein-ligand binding affinity. Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes. However, existing solutions usually treat protein-ligand complexes as topological graph data, thus the biomolecular structural information is not fully utilized. The essential long-range interactions among atoms are also neglected in GNN models. To this end, we propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool). Specifically, PGAL iteratively performs the node-edge aggregation process to update embeddings of nodes and edges while preserving the distance and angle information among atoms. Then, PiPool is adopted to gather interactive edges with a subsequent reconstruction loss to reflect the global interactions. Exhaustive experimental study on two benchmarks verifies the superiority of SIGN.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 21 Jul 2021.

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17 of 48 references.

References · 17

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2018How Powerful are Graph Neural Networks?cited by 4,393
  4. 2020Directional Message Passing for Molecular Graphscited by 561
  5. 2019Analyzing Learned Molecular Representations for Property Predictioncited by 375
  6. 2020MoFlow: An Invertible Flow Model for Generating Molecular Graphscited by 185
  7. 2018DeepDTA: Deep Drug-Target Binding Affinity Predictioncited by 173
  8. 2020Molecule Attention Transformercited by 92
  9. 2020ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Predictioncited by 58
  10. 2016Protein-Ligand Scoring with Convolutional Neural Networkscited by 57
  11. 2018Graph Transformation Policy Network for Chemical Reaction Predictioncited by 56
  12. 2017Development and evaluation of a deep learning model for protein-ligand binding affinity predictioncited by 51
5 more
  1. 2015AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discoverycited by 38
  2. 2020Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Networkcited by 37
  3. 2020Deep Learning of High-Order Interactions for Protein Interface Predictioncited by 22
  4. 2021Drug Package Recommendation via Interaction-aware Graph Inductioncited by 14
  5. 2020Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Predictioncited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 18; 2023: 13; 2024: 13; 2025: 9; 2026: 4.

60 papers.

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  1. 2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 144
  2. 2021Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Dockingcited by 82
  3. 2022One Transformer Can Understand Both 2D & 3D Molecular Datacited by 77
  4. 2021GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Predictioncited by 61
  5. 2022DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotationscited by 54
  6. 2022Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunitiescited by 52
  7. 2022Structure-based drug design with geometric deep learningcited by 40
  8. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  9. 2024Rethinking Propagation for Unsupervised Graph Domain Adaptationcited by 31
  10. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  11. 2022Graph Neural Networks for Moleculescited by 21
  12. 2023Protein-ligand binding representation learning from fine-grained interactionscited by 14
38 more
  1. 2024Graph Neural Networks in Histopathology: Emerging Trends and Future Directionscited by 13
  2. 2025Graph Neural Networks in Modern AI-aided Drug Discoverycited by 12
  3. 2022Learning Neural Set Functions Under the Optimal Subset Oraclecited by 10
  4. 2023A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systemscited by 10
  5. 2023Deep Prompt Tuning for Graph Transformerscited by 8
  6. 2022Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinitycited by 7
  7. 2022SS-GNN: A Simple-Structured Graph Neural Network for Affinity Predictioncited by 7
  8. 2023Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Predictioncited by 7
  9. 2022Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexescited by 6
  10. 2022HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Predictioncited by 6
  11. 2022Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modelingcited by 5
  12. 2024GUNDAM: Aligning Large Language Models with Graph Understandingcited by 5
  13. 2023Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structurecited by 4
  14. 2023An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Trainingcited by 4
  15. 2024Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?cited by 4
  16. 2024Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?cited by 4
  17. 2024CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Predictioncited by 3
  18. 2024Binding Affinity Prediction: From Conventional to Machine Learning-Based Approachescited by 3
  19. 2025FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity predictioncited by 3
  20. 2025Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibrationcited by 3
  21. 2022From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learningcited by 2
  22. 2023Geometric Deep Learning for Structure-Based Drug Design: A Surveycited by 2
  23. 2023ETDock: A Novel Equivariant Transformer for Protein-Ligand Dockingcited by 2
  24. 2024HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learningcited by 2
  25. 2025Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Reviewcited by 2
  26. 2021Tackling the Local Bias in Federated Graph Learningcited by 1
  27. 2022Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gaincited by 1
  28. 2022High Performance of Gradient Boosting in Binding Affinity Predictioncited by 1
  29. 2023Multi-Temporal Relationship Inference in Urban Areascited by 1
  30. 2023BeeTLe: An Imbalance-Aware Deep Sequence Model for Linear B-Cell Epitope Prediction and Classification with Logit-Adjusted Lossescited by 1
  31. 2023HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discoverycited by 1
  32. 2024Enhancing Neural Subset Selection: Integrating Background Information into Set Representationscited by 1
  33. 2025PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelscited by 1
  34. 2025Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priorscited by 1
  35. 2026HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Predictioncited by 1
  36. 2022Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategycited by 0
  37. 2022Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Networkcited by 0
  38. 2023Building explainable graph neural network by sparse learning for the drug-protein binding predictioncited by 0

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