论文标题
DeepSurf：一种基于表面的深度学习方法，用于预测蛋白质配体结合位点
DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins
论文作者
论文摘要
蛋白质上潜在的可药物结合位点的知识是朝着发现新药物的重要初步步骤。通过遵循深度学习领域的最新主要进步以及利用适当数据的增加，可以提高此类领域的计算预测。在本文中，提出了一种用于预测潜在结合位点的新型计算方法，称为DeepSurf。 DeepSurf结合了一个基于表面的表示，其中将许多3D体素化的网格放在蛋白质的表面上，并带有最先进的深度学习体系结构。在经过大型SCPDB数据库的培训之后，DeepSurf通过超越其所有主要的基于深度学习的竞争对手，同时获得一组传统的非DATA驱动方法，在三个不同的测试数据集上展示了出色的结果。
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.
