OnionNet-2：基于残基-原子接触壳的蛋白质-配体结合亲和力预测卷积神经网络模型
OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity based on Residue-Atom Contacting Shells
摘要：
虚拟筛选中的一个关键任务是准确预测蛋白质-配体复合物的结合亲和力（$\triangle$ $G$）。最近，由于深度学习（DL）从原始数据中提取有用特征的非凡能力，它显著提高了评分函数的预测准确性。然而，为了提高预测精度和降低计算成本，在许多方面还需要付出更多的努力。在这项研究中，我们提出了一个简单的评分函数（称为OnionNet-2）基于卷积神经网络预测$\triangle$ $G$。蛋白质与配体相互作用的特征在于蛋白质残基与配体原子在多个距离壳层中的接触数量。与已发布的模型相比，OnionNet-2的功效被证明是两个广泛使用的数据集CASF-2016和CASF-2013基准的最佳。OnionNet-2模型通过对接计划的非实验诱饵结构和CSAR NRC-HiQ数据集（CSAR提供的高质量数据集）进行了进一步验证，取得了很大的成功。因此，我们的研究提供了一个简单而有效的评分函数预测蛋白质-配体结合自由能。
英文摘要：
One key task in virtual screening is to accurately predict the binding affinity ($\triangle$$G$) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict $\triangle$$G$. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.
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