Dataset of bond dissociation energies

This directory consists of the data files needed to train BonDNet. Note, further processing might be needed to extract the necessary information, since this is not formatted the same as what BonDNet expects. See the below link for what BonDNet expects: https://github.com/mjwen/bondnet/tree/master/bondnet/scripts/examples/train Files molecules.sdf All molecules in the dataset are stored in this file. The file is in SDF format, which can be read by RdKit, Open Babel or other cheminformatics tools. molecule_attributes.yaml This consists of additional attributes for each molecule in the dataset. Note, they are not used in the BonDNet paper. reactions.yaml This file consists of all the bond dissociation reactions, a reaction may look like this: - atom_mapping: - 0: 0 1: 2 2: 1 - 0: 3 bond_mapping: - 0: 2 1: 0 2: 3 - {} id: 5f07dfcc578f819b3d12721e_broken_bond-0-3_species-C-H index: 1 products: - 8251 - 97 reactants: - 7943 value: 4.331072762383323 Here are the key fields: reactants: ID of the reactant molecules (in the molecules.sdf file) products: ID of the product molecules (in the molecules.sdf file) value: The bond dissociation energy of the reaction in eV atom_mapping: The mapping of atoms in the reactants and products. The first index is the reactant index, the second index is the product index. bond_mapping: The mapping of bonds in the reactants and products. The first index is the reactant index, the second index is the product index. Reference Wen, Mingjian, Samuel M. Blau, Evan Walter Clark Spotte-Smith, Shyam Dwaraknath, and Kristin A. Persson. "BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules." Chemical science 12, no. 5 (2021): 1858-1868.本目录包含训练BonDNet所需的数据文件。请注意，由于本数据集的格式与BonDNet的预期输入格式并不一致，需进行进一步处理方可提取所需信息。有关BonDNet的预期输入格式，请参阅以下链接：https://github.com/mjwen/bondnet/tree/master/bondnet/scripts/examples/train ### 文件列表 1. `molecules.sdf`：数据集中的全部分子均存储于此文件。该文件采用SDF格式，可通过RdKit、Open Babel或其他化学信息学（cheminformatics）工具读取。 2. `molecule_attributes.yaml`：本文件包含数据集中各分子的附加属性。请注意，这些属性并未在BonDNet的相关研究论文中使用。 3. `reactions.yaml`：本文件包含所有键解离反应，一个典型的反应格式示例如下： yaml - atom_mapping: - 0: 0 1: 2 2: 1 - 0: 3 bond_mapping: - 0: 2 1: 0 2: 3 - {} id: 5f07dfcc578f819b3d12721e_broken_bond-0-3_species-C-H index: 1 products: - 8251 - 97 reactants: - 7943 value: 4.331072762383323 以下为各关键字段的详细说明： - `reactants`：反应物分子的ID（对应`molecules.sdf`文件中的分子ID） - `products`：产物分子的ID（对应`molecules.sdf`文件中的分子ID） - `value`：该反应的键解离能，单位为电子伏特（eV） - `atom_mapping`：反应物与产物间的原子映射关系，第一级索引为反应物的索引，第二级索引为产物的索引 - `bond_mapping`：反应物与产物间的化学键映射关系，第一级索引为反应物的索引，第二级索引为产物的索引 ### 参考文献 Wen, Mingjian、Samuel M. Blau、Evan Walter Clark Spotte-Smith、Shyam Dwaraknath 与 Kristin A. Persson. "BonDNet：用于带电分子键解离能预测的图神经网络（graph neural network）", *Chemical Science* 12, no. 5 (2021): 1858-1868.