Protein-ligand complex structures have been utilised to design benchmark
machine learning methods that perform important tasks related to drug design
such as receptor binding site detection, small molecule docking and binding
affinity prediction. However, these methods are usually trained on only ligand
bound (or holo) conformations of the protein and therefore are not guaranteed
to perform well when the protein structure is in its native unbound
conformation (or apo), which is usually the conformation available for a newly
identified receptor. A primary reason for this is that the local structure of
the binding site usually changes upon ligand binding. To facilitate solutions
for this problem, we propose a dataset called APObind that aims to provide apo
conformations of proteins present in the PDBbind dataset, a popular dataset
used in drug design. Furthermore, we explore the performance of methods
specific to three use cases on this dataset, through which, the importance of
validating them on the APObind dataset is demonstrated.

该研究提出了一个名为 APObind 的数据集，旨在为药物设计中使用的 PDBbind 数据集中的蛋白质提供 apo 构象，并探索了该数据集的三种用例。该数据集的重要性已经得到验证，可以用于训练与蛋白质的本地结构和特定配体无关的机器学习模型。

APObind: 无配体蛋白构象的数据集，用于机器学习在新药设计中的应用

APObind: A Dataset of Ligand Unbound Protein Conformations for Machine  Learning Applications in De Novo Drug Design

Drug discovery often relies on the successful prediction of protein-ligand
binding affinity. Recent advances have shown great promise in applying graph
neural networks (GNNs) for better affinity prediction by learning the
representations of protein-ligand complexes. However, existing solutions
usually treat protein-ligand complexes as topological graph data, thus the
biomolecular structural information is not fully utilized. The essential
long-range interactions among atoms are also neglected in GNN models. To this
end, we propose a structure-aware interactive graph neural network (SIGN) which
consists of two components: polar-inspired graph attention layers (PGAL) and
pairwise interactive pooling (PiPool). Specifically, PGAL iteratively performs
the node-edge aggregation process to update embeddings of nodes and edges while
preserving the distance and angle information among atoms. Then, PiPool is
adopted to gather interactive edges with a subsequent reconstruction loss to
reflect the global interactions. Exhaustive experimental study on two
benchmarks verifies the superiority of SIGN.

本研究提出了一种结构感知交互图神经网络 (SIGN)，用于预测蛋白质 - 配体复合物的亲和力。SIGN 包含极性启发式图注意力层（PGAL）和配对交互池（PiPool）两个部分，能充分利用生物分子结构信息并考虑原子之间的长程相互作用，在两个基准测试中显示其优越性。

基于结构感知的交互式图神经网络用于预测蛋白质 - 配体结合亲和力

Structure-aware Interactive Graph Neural Networks for the Prediction of  Protein-Ligand Binding Affinity

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。