# GEFA: Early Fusion Approach in Drug-Target Affinity Prediction

arXiv: https://arxiv.org/abs/2009.12146 · HTML: https://mlsanity.com/paper/2009.12146/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction](https://mlsanity.com/paper/2103.12983/) (2021)
- [Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity](https://mlsanity.com/paper/2203.11458/) (2022)
- [ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling](https://mlsanity.com/paper/2403.12995/) (2024)
- [Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring](https://mlsanity.com/paper/2202.01195/) (2022)
- [MSCoTDet: Language-driven Multi-modal Fusion for Improved Multispectral Pedestrian Detection](https://mlsanity.com/paper/2403.15209/) (2024)
- [ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion](https://mlsanity.com/paper/2412.19589/) (2024)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)
- [Uncertainty-Aware Hardware Trojan Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2401.09479/) (2024)
- [Learning to Discover Medicines](https://mlsanity.com/paper/2202.07096/) (2022)
- [Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning](https://mlsanity.com/paper/2212.00015/) (2022)
- [Heterogeneous networks in drug-target interaction prediction](https://mlsanity.com/paper/2504.16152/) (2025)
- [A Graph-in-Graph Learning Framework for Drug-Target Interaction Prediction](https://mlsanity.com/paper/2507.11757/) (2025)
- [Towards Precision Protein-Ligand Affinity Prediction Benchmark: A Complete and Modification-Aware DAVIS Dataset](https://mlsanity.com/paper/2512.00708/) (2025)
- [Chemical and geometric representation fidelity improves drug--target affinity prediction](https://mlsanity.com/paper/2609.13230/) (2026)
- [CurvFlow-DTA: dual-graph discrete Ricci curvature flow for drug--target affinity prediction](https://mlsanity.com/paper/2609.22862/) (2026)

## Abstract (arXiv)

Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.