# Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity

arXiv: https://arxiv.org/abs/2203.11458 · HTML: https://mlsanity.com/paper/2203.11458/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 82 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Simple and Deep Graph Convolutional Networks](https://mlsanity.com/paper/2007.02133/) (2020)
- [DropEdge: Towards Deep Graph Convolutional Networks on Node Classification](https://mlsanity.com/paper/1907.10903/) (2019)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Large-Scale Learnable Graph Convolutional Networks](https://mlsanity.com/paper/1808.03965/) (2018)
- [MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction](https://mlsanity.com/paper/2004.11424/) (2020)
- [Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity](https://mlsanity.com/paper/2107.10670/) (2021)
- [Protein-Ligand Scoring with Convolutional Neural Networks](https://mlsanity.com/paper/1612.02751/) (2016)
- [DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks](https://mlsanity.com/paper/1806.07537/) (2018)
- [Development and evaluation of a deep learning model for protein-ligand binding affinity prediction](https://mlsanity.com/paper/1712.07042/) (2017)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2010.11711/) (2020)
- [GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions](https://mlsanity.com/paper/2005.05537/) (2020)
- [GEFA: Early Fusion Approach in Drug-Target Affinity Prediction](https://mlsanity.com/paper/2009.12146/) (2020)
- [DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2003.13902/) (2020)
- [Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts](https://mlsanity.com/paper/1912.12553/) (2019)
- [Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction](https://mlsanity.com/paper/2006.14002/) (2020)

## Built on by (most cited first, 7 of 7)

- [HGTDP-DTA: Hybrid Graph-Transformer with Dynamic Prompt for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2406.17697/) (2024)
- [Towards a Better Model with Dual Transformer for Drug Response Prediction](https://mlsanity.com/paper/2210.17401/) (2022)
- [ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking](https://mlsanity.com/paper/2310.08061/) (2023)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation](https://mlsanity.com/paper/2505.11529/) (2025)
- [BSL: A Unified and Generalizable Multitask Learning Platform for Virtual Drug Discovery from Design to Synthesis](https://mlsanity.com/paper/2508.01195/) (2025)
- [Recent Advances in Deep Learning-Based Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2608.13797/) (2026)

## Abstract (arXiv)

The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction. Recently, numerous deep learning-based computational methods have been proposed to predict the binding affinities between drugs and targets benefiting from their satisfactory performance. However, the previous works mainly focus on encoding biological features and chemical structures of drugs and targets, with a lack of exploiting the essential topological information from the drug-target affinity network. In this paper, we propose a novel hierarchical graph representation learning model for the drug-target binding affinity prediction, namely HGRL-DTA. The main contribution of our model is to establish a hierarchical graph learning architecture to incorporate the intrinsic properties of drug/target molecules and the topological affinities of drug-target pairs. In this architecture, we adopt a message broadcasting mechanism to integrate the hierarchical representations learned from the global-level affinity graph and the local-level molecular graph. Besides, we design a similarity-based embedding map to solve the cold start problem of inferring representations for unseen drugs and targets. Comprehensive experimental results under different scenarios indicate that HGRL-DTA significantly outperforms the state-of-the-art models and shows better model generalization among all the scenarios.