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
this http URL

本研究基于结构的配体发现，利用深度神经网络和三维卷积技术实现，通过对配体 - 受体复合物进行建模，准确地估算它们间的结合亲和力，多项测试表明本方法优于经典评分函数。