Gou Qiaolin, Su Qun, Wang Jike, Zhang Hui, Sun Huiyong, Zhang Xujun, Jiang Linlong, Fang Meijing, Kang Yu, Liu Huanxiang, Hou Tingjun, Hsieh Chang-Yu
Faculty of Applied Science, Macao Polytechnic University, Macao 999078, China.
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
J Chem Inf Model. 2025 Jun 18. doi: 10.1021/acs.jcim.5c00602.
Atomic charge is a fundamental quantum chemical property essential for advancing drug design and discovery. Although quantum mechanics (QM) methods offer the highest level of accuracy, their computational demands scale quadratically with the number of atoms, limiting their practicality for large-scale applications. In light of this, empirical and semiempirical methods have been introduced to improve computational efficiency, albeit often at the expense of accuracy. The advent of artificial intelligence has witnessed a growing application of machine learning (ML) techniques to accelerate atomic charge predictions. However, existing ML models often suffer from low accuracy and limited generalization capabilities. To address these challenges, we introduce an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision. This model introduces a sophisticated global graph attention mechanism, enabling it to capture charge contributions across multiple scales. By utilizing a combination of structural symmetry-preserving transformations and multiscale attention, our approach not only preserves the inherent symmetries of molecular structures but also substantially improves the model's accuracy, generalization, and robustness in complex scenarios. Our empirical analyses demonstrate that, compared to leading baseline models, the proposed model improves charge prediction accuracy by over 40% on average across various charge-calculation schemes. Remarkably, the model achieves superior performance on the external RESP (restrained electrostatic potential) test data sets, with a 54.6% improvement over the baseline. Additionally, we evaluated our charge model under the setting of virtual screening, where it outperforms both the OPLS3 charges and baseline deep learning models across all evaluation metrics, highlighting its extensive potential for scientific discovery.
原子电荷是推进药物设计与发现所必需的一种基本量子化学性质。尽管量子力学（QM）方法提供了最高水平的准确性，但其计算需求随原子数量呈二次方增长，限制了它们在大规模应用中的实用性。鉴于此，已引入经验方法和半经验方法来提高计算效率，尽管这往往是以牺牲准确性为代价。随着人工智能的出现，机器学习（ML）技术在加速原子电荷预测方面的应用越来越多。然而，现有的ML模型往往准确性较低且泛化能力有限。为应对这些挑战，我们引入了一种先进的等变图注意力神经网络，专门设计用于高精度地模拟远程原子静电相互作用。该模型引入了一种复杂的全局图注意力机制，使其能够捕捉多个尺度上的电荷贡献。通过结合保持结构对称性的变换和多尺度注意力，我们的方法不仅保留了分子结构的固有对称性，而且在复杂场景中显著提高了模型的准确性、泛化能力和鲁棒性。我们的实证分析表明，与领先的基线模型相比，所提出的模型在各种电荷计算方案下平均将电荷预测准确性提高了40%以上。值得注意的是，该模型在外部RESP（受限静电势）测试数据集上表现优异，比基线提高了54.6%。此外，我们在虚拟筛选设置下评估了我们的电荷模型，在所有评估指标上它都优于OPLS3电荷和基线深度学习模型，突出了其在科学发现方面的广泛潜力。
