# Development and evaluation of a deep learning model for protein-ligand binding affinity prediction

arXiv: https://arxiv.org/abs/1712.07042 · HTML: https://mlsanity.com/paper/1712.07042/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 30 of 51)

- [One Transformer Can Understand Both 2D & 3D Molecular Data](https://mlsanity.com/paper/2210.01765/) (2022)
- [Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity](https://mlsanity.com/paper/2107.10670/) (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 Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks](https://mlsanity.com/paper/2111.15144/) (2021)
- [A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems](https://mlsanity.com/paper/2311.11228/) (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)
- [HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2212.12440/) (2022)
- [3D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Information](https://mlsanity.com/paper/2309.17366/) (2023)
- [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)
- [Machine Learning Small Molecule Properties in Drug Discovery](https://mlsanity.com/paper/2308.12354/) (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)
- [S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search](https://mlsanity.com/paper/2409.07462/) (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)
- [APObind: A Dataset of Ligand Unbound Protein Conformations for Machine Learning Applications in De Novo Drug Design](https://mlsanity.com/paper/2108.09926/) (2021)
- [From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning](https://mlsanity.com/paper/2208.10230/) (2022)
- [Geometric Deep Learning for Structure-Based Drug Design: A Survey](https://mlsanity.com/paper/2306.11768/) (2023)
- [Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook](https://mlsanity.com/paper/2310.03086/) (2023)
- [FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation](https://mlsanity.com/paper/2311.02326/) (2023)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe](https://mlsanity.com/paper/2105.14224/) (2021)
- [Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction](https://mlsanity.com/paper/2212.09991/) (2022)
- [HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery](https://mlsanity.com/paper/2311.12814/) (2023)
- [Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction](https://mlsanity.com/paper/2501.16391/) (2025)
- [DynamicDTA: Drug-Target Binding Affinity Prediction Using Dynamic Descriptors and Graph Representation](https://mlsanity.com/paper/2505.11529/) (2025)
- [DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2507.06366/) (2025)

## Abstract (arXiv)

Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to "learn" to extract features that are relevant for the task at hand. We have developed a novel deep neural network estimating the binding affinity of ligand-receptor complexes. The complex is represented with a 3D grid, and the model utilizes a 3D convolution to produce a feature map of this representation, treating the atoms of both proteins and ligands in the same manner. Our network was tested on the CASF "scoring power" benchmark and Astex Diverse Set and outperformed classical scoring functions. The model, together with usage instructions and examples, is available as a git repository at http://gitlab.com/cheminfIBB/pafnucy