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

arXiv: https://arxiv.org/abs/2107.10670 · HTML: https://mlsanity.com/paper/2107.10670/

## 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.

## Builds on (most cited first, 17 of 17)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [Analyzing Learned Molecular Representations for Property Prediction](https://mlsanity.com/paper/1904.01561/) (2019)
- [MoFlow: An Invertible Flow Model for Generating Molecular Graphs](https://mlsanity.com/paper/2006.10137/) (2020)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Molecule Attention Transformer](https://mlsanity.com/paper/2002.08264/) (2020)
- [ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction](https://mlsanity.com/paper/2007.03196/) (2020)
- [Protein-Ligand Scoring with Convolutional Neural Networks](https://mlsanity.com/paper/1612.02751/) (2016)
- [Graph Transformation Policy Network for Chemical Reaction Prediction](https://mlsanity.com/paper/1812.09441/) (2018)
- [Development and evaluation of a deep learning model for protein-ligand binding affinity prediction](https://mlsanity.com/paper/1712.07042/) (2017)
- [AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery](https://mlsanity.com/paper/1510.02855/) (2015)
- [Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network](https://mlsanity.com/paper/2003.02944/) (2020)
- [Deep Learning of High-Order Interactions for Protein Interface Prediction](https://mlsanity.com/paper/2007.09334/) (2020)
- [Drug Package Recommendation via Interaction-aware Graph Induction](https://mlsanity.com/paper/2102.03577/) (2021)
- [Distance-aware Molecule Graph Attention Network for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2012.09624/) (2020)

## Built on by (most cited first, 30 of 60)

- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking](https://mlsanity.com/paper/2111.07786/) (2021)
- [One Transformer Can Understand Both 2D & 3D Molecular Data](https://mlsanity.com/paper/2210.01765/) (2022)
- [GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction](https://mlsanity.com/paper/2109.11730/) (2021)
- [DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations](https://mlsanity.com/paper/2201.09637/) (2022)
- [Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2204.07321/) (2022)
- [Structure-based drug design with geometric deep learning](https://mlsanity.com/paper/2210.11250/) (2022)
- [Distributed Graph Neural Network Training: A Survey](https://mlsanity.com/paper/2211.00216/) (2022)
- [Rethinking Propagation for Unsupervised Graph Domain Adaptation](https://mlsanity.com/paper/2402.05660/) (2024)
- [Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs](https://mlsanity.com/paper/2311.14324/) (2023)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Protein-ligand binding representation learning from fine-grained interactions](https://mlsanity.com/paper/2311.16160/) (2023)
- [Graph Neural Networks in Histopathology: Emerging Trends and Future Directions](https://mlsanity.com/paper/2406.12808/) (2024)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [Learning Neural Set Functions Under the Optimal Subset Oracle](https://mlsanity.com/paper/2203.01693/) (2022)
- [A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems](https://mlsanity.com/paper/2311.11228/) (2023)
- [Deep Prompt Tuning for Graph Transformers](https://mlsanity.com/paper/2309.10131/) (2023)
- [Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity](https://mlsanity.com/paper/2203.11458/) (2022)
- [SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction](https://mlsanity.com/paper/2206.07015/) (2022)
- [Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction](https://mlsanity.com/paper/2306.04886/) (2023)
- [Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes](https://mlsanity.com/paper/2206.02789/) (2022)
- [HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2212.12440/) (2022)
- [Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling](https://mlsanity.com/paper/2209.13014/) (2022)
- [GUNDAM: Aligning Large Language Models with Graph Understanding](https://mlsanity.com/paper/2409.20053/) (2024)
- [Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure](https://mlsanity.com/paper/2301.10956/) (2023)
- [An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training](https://mlsanity.com/paper/2308.15602/) (2023)
- [Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?](https://mlsanity.com/paper/2406.19755/) (2024)
- [Graph Neural Networks: A suitable Alternative to MLPs in Latent 3D Medical Image Classification?](https://mlsanity.com/paper/2407.17219/) (2024)
- [CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction](https://mlsanity.com/paper/2409.03773/) (2024)
- [Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches](https://mlsanity.com/paper/2410.00709/) (2024)

## 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.