Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning: Raw Data and Generation Script

Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices. It is necessary to make them available in a computer friendly format and to curate them. Furthermore, the raw data need to be enriched with physicochemical information about each molecule and when such information does not allow to discriminate molecules, some additional data is needed. We present a dataset built from data gathered from the literature. The dataset contains both the original raw data from the articles and the enriched ones. We also provide the scripts used to curate and enrich the raw data.确定环糊精（cyclodextrin）与客体分子之间的结合常数，是工业与学术领域诸多应用中的一项核心工作。然而该类任务耗时冗长、操作繁琐，且需同时获取两种分子的样品。目前已有大量结合常数及相关数据见诸文献，此类数据的公开性使得借助机器学习技术预测结合常数成为可行方案。但这类数据大多仅以表格或附录形式散见于学术论文中，亟需将其转换为计算机友好格式并开展规范化整理。此外，原始数据还需补充每种分子的理化信息；若现有理化信息不足以区分不同分子，则需补充额外数据。本研究构建了一套基于文献采集数据的数据集，既包含学术论文中的原始数据，也包含经过补充完善后的数据集。同时，本研究还提供了用于规范化整理与补充原始数据的脚本程序。