We present a simple, modular graph-based convolutional neural network that
takes structural information from protein-ligand complexes as input to generate
models for activity and binding mode prediction. Complex structures are
generated by a standard docking procedure and fed into a dual-graph
architecture that includes separate sub-networks for the ligand bonded topology
and the ligand-protein contact map. This network division allows contributions
from ligand identity to be distinguished from effects of protein-ligand
interactions on classification. We show, in agreement with recent literature,
that dataset bias drives many of the promising results on virtual screening
that have previously been reported. However, we also show that our neural
network is capable of learning from protein structural information when, as in
the case of binding mode prediction, an unbiased dataset is constructed. We
develop a deep learning model for binding mode prediction that uses docking
ranking as input in combination with docking structures. This strategy mirrors
past consensus models and outperforms the baseline docking program in a variety
of tests, including on cross-docking datasets that mimic real-world docking use
cases. Furthermore, the magnitudes of network predictions serve as reliable
measures of model confidence

提出了一种基于图的卷积神经网络，该网络以蛋白质 - 配体复合物的结构信息作为输入，生成活性和结合模式预测模型，并开发了一种深度学习模型，用于结合模式预测，并在各种测试中优于基线对接程序。