# Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference

arXiv: https://arxiv.org/abs/2005.07704 · HTML: https://mlsanity.com/paper/2005.07704/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2020.

## Built on by (most cited first, 30 of 35)

- [DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening](https://mlsanity.com/paper/2310.06367/) (2023)
- [Deep Molecular Representation Learning via Fusing Physical and Chemical Information](https://mlsanity.com/paper/2112.04624/) (2021)
- [PharmAgents: Building a Virtual Pharma with Large Language Model Agents](https://mlsanity.com/paper/2503.22164/) (2025)
- [Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation](https://mlsanity.com/paper/2301.10814/) (2023)
- [Multi-Objective Latent Space Optimization of Generative Molecular Design Models](https://mlsanity.com/paper/2203.00526/) (2022)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks](https://mlsanity.com/paper/2111.15144/) (2021)
- [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)
- [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)
- [Geometric Graph Learning with Extended Atom-Types Features for Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2301.06194/) (2023)
- [Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models](https://mlsanity.com/paper/2310.13913/) (2023)
- [A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics](https://mlsanity.com/paper/2401.15122/) (2024)
- [A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery](https://mlsanity.com/paper/2309.03919/) (2023)
- [High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models](https://mlsanity.com/paper/2104.04547/) (2021)
- [DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries](https://mlsanity.com/paper/2212.00136/) (2022)
- [SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers](https://mlsanity.com/paper/2302.07754/) (2023)
- [Improved prediction of ligand-protein binding affinities by meta-modeling](https://mlsanity.com/paper/2310.03946/) (2023)
- [Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches](https://mlsanity.com/paper/2410.00709/) (2024)
- [Recent Developments in GNNs for Drug Discovery](https://mlsanity.com/paper/2506.01302/) (2025)
- [Geometric Deep Learning for Structure-Based Drug Design: A Survey](https://mlsanity.com/paper/2306.11768/) (2023)
- [Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions](https://mlsanity.com/paper/2308.09086/) (2023)
- [Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model](https://mlsanity.com/paper/2403.13244/) (2024)
- [Hashing based Contrastive Learning for Virtual Screening](https://mlsanity.com/paper/2407.19790/) (2024)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [High Performance of Gradient Boosting in Binding Affinity Prediction](https://mlsanity.com/paper/2205.07023/) (2022)
- [Multi-level Protein Representation Learning for Blind Mutational Effect Prediction](https://mlsanity.com/paper/2306.04899/) (2023)
- [HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery](https://mlsanity.com/paper/2311.12814/) (2023)
- [Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy](https://mlsanity.com/paper/2204.12586/) (2022)

## Abstract (arXiv)

Predicting accurate protein-ligand binding affinity is important in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite the recent advances in the deep convolutional and graph neural network based approaches, the model performance depends on the input data representation and suffers from distinct limitations. It is natural to combine complementary features and their inference from the individual models for better predictions. We present fusion models to benefit from different feature representations of two neural network models to improve the binding affinity prediction. We demonstrate effectiveness of the proposed approach by performing experiments with the PDBBind 2016 dataset and its docking pose complexes. The results show that the proposed approach improves the overall prediction compared to the individual neural network models with greater computational efficiency than related biophysics based energy scoring functions. We also discuss the benefit of the proposed fusion inference with several example complexes. The software is made available as open source at https://github.com/llnl/fast.