# DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins

arXiv: https://arxiv.org/abs/2002.05643 · HTML: https://mlsanity.com/paper/2002.05643/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2020.

## Built on by (most cited first, 17 of 17)

- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications](https://mlsanity.com/paper/2403.00485/) (2024)
- [EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction](https://mlsanity.com/paper/2302.12177/) (2023)
- [VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification](https://mlsanity.com/paper/2404.07194/) (2024)
- [Learning Harmonic Molecular Representations on Riemannian Manifold](https://mlsanity.com/paper/2303.15520/) (2023)
- [DSDP: A Blind Docking Strategy Accelerated by GPUs](https://mlsanity.com/paper/2303.09916/) (2023)
- [UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection](https://mlsanity.com/paper/2506.03237/) (2025)
- [Structure-based drug discovery with deep learning](https://mlsanity.com/paper/2212.13295/) (2022)
- [Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence](https://mlsanity.com/paper/2312.03016/) (2023)
- [Geometric Deep Learning for Structure-Based Drug Design: A Survey](https://mlsanity.com/paper/2306.11768/) (2023)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)
- [Recent advances in interpretable machine learning using structure-based protein representations](https://mlsanity.com/paper/2409.17726/) (2024)
- [Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation](https://mlsanity.com/paper/2303.08818/) (2023)
- [Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model](https://mlsanity.com/paper/2511.16675/) (2025)
- [GDEGAN: Gaussian Dynamic Equivariant Graph Attention Network for Ligand Binding Site Prediction](https://mlsanity.com/paper/2603.19817/) (2026)
- [SpliceBind: Isoform-Aware Prediction of Binding Pocket Druggability](https://mlsanity.com/paper/2606.04020/) (2026)
- [Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction](https://mlsanity.com/paper/2606.23830/) (2026)

## Abstract (arXiv)

The knowledge of potentially druggable binding sites on proteins is an important preliminary step towards the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the deep learning field and by exploiting the increasing availability of proper data. In this paper, a novel computational method for the prediction of potential binding sites is proposed, called DeepSurf. DeepSurf combines a surface-based representation, where a number of 3D voxelized grids are placed on the protein's surface, with state-of-the-art deep learning architectures. After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors, while attaining competitive performance to a set of traditional non-data-driven approaches.