AttenGpKa:使用图形神经网络和分子拓学的溶解酸度的通用预测器
Hongle An1,2, Xuyang Liu1,2, Wensheng Cai1,2
1Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China.
Journal of chemical information and modeling
|July 10, 2024
概括
一个新的通用框架,AttenGpKa,使用图形神经网络准确地预测不同溶剂的酸解离常数 (pKa). 这通过统一溶液和溶剂建模来推进化学行为预测和合成设计.
科学领域:
- 计算化学计算化学
- 机器学习 机器学习
- 物理化学 物理化学
背景情况:
- 预测酸解离常数 (pKa) 对化学应用至关重要.
- 现有的机器学习模型由于数据限制和单独的域建模,难以预测非水性溶剂中的溶解酸度.
研究的目的:
- 开发一个通用框架,AtenGpKa,用于准确预测各种溶剂的溶解酸度.
- 为了统一的预测模型,使用分子拓统一地处理溶液和溶剂.
主要方法:
- 在AttenGpKa框架内利用图形神经网络和注意力机制.
- 在iBonD数据库中的26522个实验pKa值上训练模型,涵盖60种纯溶剂和混合溶剂.
- 使用溶解物和溶剂分子编码的自适应融合来模拟溶剂效应.
主要成果:
- AttenGpKa成功地同时预测水,非水和混合溶剂中的化合物的pKa值.
- 该模型在广泛的验证测试中展示了强大的概括能力.
- 可解释性研究证实了模型对电子和溶剂效应的有效学习.
结论:
- AttenGpKa提供了一个高度通用和准确的方法来预测溶解酸度.
- 该框架通过统一溶液和溶剂处理来克服以前方法的局限性.
- 一个免费可用的软件促进了AtenGpKa在化学研究中的实际应用.
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