Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .

中文翻译：

---

QuanDB：用于增强 3D 分子表示学习的量子化学性质数据库

先前的研究表明，分子的三维（3D）几何和电子结构在决定其关键特性和分子间相互作用方面发挥着至关重要的作用。因此，有必要建立一个包含分子最稳定的3D几何构象和电子结构的量子化学（QC）特性数据库。在这项研究中，开发了一个名为 QuanDB 的高质量 QC 特性数据库，其中包括结构多样的分子实体，并具有用户友好的界面。目前，QuanDB 包含来自公共数据库和科学文献的 154,610 种化合物，以及 10,125 个支架。元素组成包含九种元素：H、C、O、N、P、S、F、Cl 和 Br。对于每个分子，QuanDB 提供 53 个全局和 5 个局部 QC 属性以及最稳定的 3D 构象。这些性质分为三类：几何结构、电子结构和热力学。分别应用B3LYP-D3(BJ)/6-311G(d)/SMD/water和B3LYP-D3(BJ)/def2-TZVP/SMD/water理论层面的几何结构优化和单点能量计算确保QC属性的高精度计算，计算成本超过107核心小时。 QuanDB 提供高价值的几何和电子结构信息，用于分子表示模型，这对于基于机器学习的分子设计至关重要，从而有助于全面描述化合物空间。作为新的高质量QC特性数据集，QuanDB有望成为机器学习模型训练和优化的基准工具，从而进一步推动新药和新材料的开发。 QuanDB 无需注册即可免费使用，网址为 https://quandb.cmdrg.com/ 。