RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks.,Journal of Chemical Information and Modeling

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RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2020-05-11 , DOI: 10.1021/acs.jcim.0c00075
Hussein Hassan-Harrirou ₁ , Ce Zhang ₁ , Thomas Lemmin _{1,

2}

Affiliation

The worldwide increase and proliferation of drug resistant microbes, coupled with the lag in new drug development, represents a major threat to human health. In order to reduce the time and cost for exploring the chemical search space, drug discovery increasingly relies on computational biology approaches. One key step in these approaches is the need for the rapid and accurate prediction of the binding affinity for potential leads. Here, we present RosENet (Rosetta Energy Neural Networks), an ensemble of three-dimensional (3D) Convolutional Neural Networks (CNNs), which combines voxelized molecular mechanics energies and molecular descriptors for predicting the absolute binding affinity of protein–ligand complexes. By leveraging the physicochemical properties captured by the molecular force field, our ensemble model achieved a Root Mean Square Error (RMSE) of 1.24 on the PDBBind v2016 core set. We also explored some limitations and the robustness of the PDBBind data set and our approach on nearly 500 structures, including structures determined by Nuclear Magnetic Resonance and virtual screening experiments. Our study demonstrated that molecular mechanics energies can be voxelized and used to help improve the predictive power of the CNNs. In the future, our framework can be extended to features extracted from other biophysical and biochemical models, such as molecular dynamics simulations.

中文翻译：

RosENet：通过与3D卷积神经网络集成利用分子力学能量来改善结合亲和力预测。

耐药菌在全球范围内的增长和扩散，再加上新药开发的滞后，对人类健康构成了重大威胁。为了减少探索化学搜索空间的时间和成本，药物发现越来越依赖于计算生物学方法。这些方法中的一个关键步骤是需要快速准确地预测对潜在先导的结合亲和力。在这里，我们提出RosENet（罗斯ETTA ê NERGY神经网络作品），是三维（3D）卷积神经网络（CNN）的集合，它结合了体素化的分子力学能和分子描述符来预测蛋白质-配体复合物的绝对结合亲和力。通过利用分子力场捕获的物理化学性质，我们的集成模型在PDBBind v2016核心集上实现了1.24的均方根误差（RMSE）。我们还探索了PDBBind数据集的局限性和稳健性，以及我们针对近500种结构（包括通过核磁共振和虚拟筛选实验确定的结构）的方法。我们的研究表明，分子力学能量可以被体素化并用于帮助提高CNN的预测能力。在将来，

更新日期：2020-06-23

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