# DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction

arXiv: https://arxiv.org/abs/1912.00318 · HTML: https://mlsanity.com/paper/1912.00318/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 14 of 14)

- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2212.12440/) (2022)
- [BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation](https://mlsanity.com/paper/2211.13979/) (2022)
- [Morphological Profiling for Drug Discovery in the Era of Deep Learning](https://mlsanity.com/paper/2312.07899/) (2023)
- [Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches](https://mlsanity.com/paper/2410.00709/) (2024)
- [Geometric Deep Learning for Structure-Based Drug Design: A Survey](https://mlsanity.com/paper/2306.11768/) (2023)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery](https://mlsanity.com/paper/2311.12814/) (2023)
- [Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation](https://mlsanity.com/paper/2504.09481/) (2025)
- [DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2507.06366/) (2025)
- [Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2509.11046/) (2025)
- [Generalization Beyond Benchmarks: Evaluating Learnable Protein-Ligand Scoring Functions on Unseen Targets](https://mlsanity.com/paper/2512.05386/) (2025)
- [Recent Developments in Structure-Based Virtual Screening Approaches](https://mlsanity.com/paper/2211.03208/) (2022)
- [RiboSphere: Learning Unified and Efficient Representations of RNA Structures](https://mlsanity.com/paper/2603.19636/) (2026)

## Abstract (arXiv)

The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts, especially a chemical compound, i.e., a ligand, and a protein. Predicting the strength of protein-ligand binding with reasonable accuracy is critical for drug discovery. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. With validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set, we demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other state-of-the-art scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.