AromTool: predicting aromatic stacking energy using an atomic neural network model
AromTool: predicting aromatic stacking energy using an atomic neural network model
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AromTool：使用原子神经网络模型预测芳香堆积能量
DOI：
10.1039/d1cp01954f
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发表时间：
2021
影响因子：
3.3
通讯作者：
Wu Ruibo
中科院分区：
文献类型：
--
作者：
He Wengan;Liang Danhong;Wang Kai;Lyu Nan;Diao Hongjuan;Wu Ruibo
芳香堆积广泛存在，在蛋白质-配体相互作用中起着重要作用。基于结构的药物设计需要自动分析几何形状并精确计算堆积相互作用能量的计算工具。在此，我们采用Behler-Parrinello神经网络（BPNN）来构建芳族堆积相互作用的预测模型，并将其进一步集成到名为AromTool的开源Python包中，用于含苯芳族堆积分析。基于广泛的测试，AromTool与DFT计算相比具有理想的精度，并且对于蛋白质-配体复合物的高通量芳香堆积分析具有出色的效率。
Aromatic stacking exists widely and plays important roles in protein–ligand interactions. Computational tools to automatically analyze the geometry and accurately calculate the energy of stacking interactions are desired for structure-based drug design. Herein, we employed a Behler–Parrinello neural network (BPNN) to build predictive models for aromatic stacking interactions and further integrated it into an open-source Python package named AromTool for benzene-containing aromatic stacking analysis. Based on extensive testing, AromTool presents desirable precision in comparison to DFT calculations and excellent efficiency for high-throughput aromatic stacking analysis of protein–ligand complexes.