TocoDecoy: a new approach to design unbiased datasets for training and benchmarking machine-learning scoring functions

This dataset file contains TocoDecoy datasets generated based on the targets and active ligands of LIT-PCBA. 1_property_filtered.zip : TD set: the ligand file name, 2D T-sne vectors, Smiles, molecular weight (MW), Wildman-Crippen partition coefficient (log P), number of rotatable bonds (RB), number of hydrogen-bond acceptors (HBA), number of hydrogen-bond donors (HBD), number of halogens (HAL), topology similarities of decoys to the seed active ligands, active label (active or inactive) and training set label (whether belongs to training set or test set) <strong>OF active ligands and their topologically dissimilar decoys</strong> CD set: the decoy conformations with low docking scores generated by docking active ligands into protein pockets using Glide, Schrödinger.本数据集文件包含基于LIT-PCBA靶点与活性配体构建的TocoDecoy数据集。其中1_property_filtered.zip包含两类数据集：TD集，涵盖活性配体及其拓扑学差异显著的诱饵分子的以下信息：配体文件名、二维t分布邻域嵌入（2D t-SNE）向量、简化分子线性输入规范（SMILES）、分子量（MW）、Wildman-Crippen分配系数（log P）、可旋转键数目（RB）、氢键受体数目（HBA）、氢键供体数目（HBD）、卤原子数目（HAL）、诱饵分子与种子活性配体的拓扑相似度、活性标签（活性/非活性）以及训练集标签（是否隶属于训练集或测试集）；CD集，即通过Schrödinger公司的Glide工具将活性配体对接至蛋白质口袋后，生成的低对接得分诱饵分子构象集合。