数据集用于量子力学探索大型药物样分子中的适配体和溶剂效应
Leonardo Medrano Sandonas1,2, Dries Van Rompaey3, Alessio Fallani4,5
1Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg. leonardo.medrano@tu-dresden.de.
Scientific data
|July 7, 2024
概括
水 (AQM) 数据集为药物样分子提供了广泛的量子力学数据,包括溶剂和分散效应. 这种量子力学数据集对于开发药物发现中先进的机器学习模型非常有价值.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 药物发现 药物发现 药物发现
背景情况:
- 准确预测分子性质需要考虑溶剂效应和分散相互作用.
- 现有的数据集往往缺乏关于大型分子这些复杂相互作用的全面数据.
研究的目的:
- 介绍水 (AQM) 数据集,一个大规模的量子力学数据集.
- 为各种分子对应物提供结构和电子信息.
- 能够深入了解溶剂效应和多体分散 (MBD) 相互作用.
主要方法:
- 对1653个分子 (59783个符合) 进行了量子力学 (QM) 计算.
- 使用PBE0+MBD理论水平进行气相计算.
- 采用PBE0+MBD与修改的Poisson-Boltzmann (MPB) 模型用于化分子.
主要成果:
- 该AQM数据集包含了40多个物理化学特性.
- 包括高达92个原子的分子数据,平均50.9个原子.
- 涵盖了气相和隐性水溶解条件.
结论:
- 该AQM数据集作为房地产建模中机器学习的基准.
- 促进大,药物相关分子的新生代.
- 解决了计算化学的关键分子-溶剂和分散相互作用.
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