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

Motivation: The knowledge of potentially druggable binding sites on proteins is an important preliminary step toward 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. Results: In this article, 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.

Automated summary

The article introduces a novel computational method called DeepSurf for predicting potential binding sites. After being trained on a large database, DeepSurf demonstrates superior results on multiple testing datasets, surpassing other deep learning methods and competing competitively with traditional approaches.