4.6 Article

A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database

期刊

JOURNAL OF CHEMINFORMATICS
卷 15, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s13321-023-00780-2

关键词

Crystallography Open Database; PubChem; Molecular perception; Chemical structure assignment

向作者/读者索取更多资源

Knowledge of the 3-dimensional structure, orientation, and interaction of chemical compounds is crucial in many fields. This publication presents an automatic pipeline to derive chemical attributes from crystallographic models and applies it to build a catalog in an open-access crystallographic database.
Knowledge about the 3-dimensional structure, orientation and interaction of chemical compounds is important in many areas of science and technology. X-ray crystallography is one of the experimental techniques capable of providing a large amount of structural information for a given compound, and it is widely used for characterisation of organic and metal-organic molecules. The method provides precise 3D coordinates of atoms inside crystals, however, it does not directly deliver information about certain chemical characteristics such as bond orders, delocalization, charges, lone electron pairs or lone electrons. These aspects of a molecular model have to be derived from crystallographic data using refined information about interatomic distances and atom types as well as employing general chemical knowledge. This publication describes a curated automatic pipeline for the derivation of chemical attributes of molecules from crystallographic models. The method is applied to build a catalogue of chemical entities in an open-access crystallographic database, the Crystallography Open Database (COD). The catalogue of such chemical entities is provided openly as a derived database. The content of this catalogue and the problems arising in the fully automated pipeline are discussed, along with the possibilities to introduce manual data curation into the process.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据