Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction methods have been proposed to speed up the calculation of high-accuracy atomic charges in recent years. However, most of them used a set of predefined molecular properties, such as molecular fingerprints, for model construction, which is knowledge-dependent and may lead to biased predictions due to the representation preference of different molecular properties used for training. To solve the problem, we present a new architecture based on graph convolutional network (GCN) and develop a high-accuracy atomic charge prediction model named DeepAtomicCharge. The new GCN architecture is designed with only the atomic properties and the connection information between the atoms in molecules and can dynamically learn and convert molecules into appropriate atomic features without any prior knowledge of the molecules. Using the designed GCN architecture, substantial improvement is achieved for the prediction accuracy of atomic charges. The average root-mean-square error (RMSE) of DeepAtomicCharge is 0.0121 e, which is obviously more accurate than that (0.0180 e) reported by the previous benchmark study on the same two external test sets. Moreover, the new GCN architecture needs much lower storage space compared with other methods, and the predicted DDEC atomic charges can be efficiently used in large-scale structure-based drug design, thus opening a new avenue for high-performance atomic charge prediction and application.

中文翻译：

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DeepAtomicCharge：一种新的基于图卷积网络的架构，用于准确预测原子电荷。

原子电荷在药物靶点识别中起着非常重要的作用。然而，使用高级量子力学 (QM) 计算计算原子电荷非常耗时。近年来，已经提出了许多基于机器学习（ML）的原子电荷预测方法来加速高精度原子电荷的计算。然而，他们中的大多数使用一组预定义的分子特性，例如分子指纹，用于模型构建，这依赖于知识，并且可能由于用于训练的不同分子特性的表示偏好而导致有偏差的预测。为了解决这个问题，我们提出了一种基于图卷积网络 (GCN) 的新架构，并开发了一种名为DeepAtomicCharge 的高精度原子电荷预测模型. 新的 GCN 架构的设计仅包含原子特性和分子中原子之间的连接信息，可以动态学习分子并将其转换为适当的原子特征，而无需对分子有任何先验知识。使用设计的GCN架构，原子电荷的预测精度得到了实质性的提高。DeepAtomicCharge的平均均方根误差 (RMSE)是 0.0121 e，这显然比之前在相同的两个外部测试集上的基准研究报告的 (0.0180 e) 更准确。此外，与其他方法相比，新的 GCN 架构需要更少的存储空间，预测的 DDEC 原子电荷可以有效地用于大规模基于结构的药物设计，从而为高性能原子电荷预测和应用开辟了新途径.