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QuanDB:一个量子化学性质数据库,旨在增强3D分子表示学习

Zhijiang Yang¹, Tengxin Huang¹, Li Pan¹

¹State Key Laboratory of NBC Protection for Civilian, Beijing, People's Republic of China.

Journal of cheminformatics

|April 29, 2024

概括

一个新的量子化学 (QC) 属性数据库, QuanDB,提供了 154,610 个具有稳定的 3D 结构和 53 个 QC 属性的多样化分子. 这一资源有助于机器学习在药物和材料发现的分子设计.

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:

分子3D结构和电子特性对于预测化学行为和相互作用至关重要.
现有的量子化学 (QC) 属性数据集可能缺乏全面的结构多样性或用户可访问性.
准确的质量控制数据对于推进分子设计中的计算化学和机器学习应用至关重要.

研究的目的:

开发一个高质量的,全面的量子化学 (QC) 属性数据库,命名为QuanDB.
为各种化学实体提供稳定的3D分子构造和广泛的QC特性.
为基于机器学习的分子设计和化学空间探索创建一个用户友好的资源.

主要方法:

来自公共数据库和文献的154,610个化合物的汇编,涵盖10,125个支架和9个元素.
计算53个全球和5个本地QC属性,包括几何结构,电子结构和热力学.
使用B3LYP-D3 ((BJ)/6-311G ((d) /SMD/水用于几何优化和B3LYP-D3 ((BJ) /def2-TZVP/SMD/水用于单点能量计算.

主要成果:

开发了QuanDB,一个包含154,610个分子的数据库,具有详细的QC属性和稳定的3D构造.
该数据库包括10,125个独特的支架,涵盖元素H,C,O,N,P,S,F,Cl和Br.
计算需要超过107个核心小时,确保所提供的几何和电子结构数据的高精度.

结论:

QuanDB为分子表示模型提供了有价值的数据,对于机器学习驱动的分子设计至关重要.
该数据库作为训练和优化化学机器学习模型的基准.
QuanDB是免费访问的,并准备加速新药和新材料的发现.

关键词:

化学信息学化学信息学数据库数据库数据库是一个数据库.机器学习是机器学习.量子化学属性量子化学属性

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