A review of optical chemical structure recognition tools

Journal of Cheminformatics

Achim Zielesny¹  Kohulan Rajan²  Christoph Steinbeck²  Henning Otto Brinkhaus² 

[1] Institute for Bioinformatics and Chemoinformatics, Westphalian University of Applied Sciences, August-Schmidt-Ring 10, 45665, Recklinghausen, Germany;Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University Jena, Lessingstr. 8, 07743, Jena, Germany;

关键词: Optical chemical structure recognition;    Named entity recognition;    Data mining;    Chemical data extraction;    Chemical structure;    Open data;    Machine learning;   

DOI  :  10.1186/s13321-020-00465-0

来源: Springer

【 摘 要 】

Structural information about chemical compounds is typically conveyed as 2D images of molecular structures in scientific documents. Unfortunately, these depictions are not a machine-readable representation of the molecules. With a backlog of decades of chemical literature in printed form not properly represented in open-access databases, there is a high demand for the translation of graphical molecular depictions into machine-readable formats. This translation process is known as Optical Chemical Structure Recognition (OCSR). Today, we are looking back on nearly three decades of development in this demanding research field. Most OCSR methods follow a rule-based approach where the key step of vectorization of the depiction is followed by the interpretation of vectors and nodes as bonds and atoms. Opposed to that, some of the latest approaches are based on deep neural networks (DNN). This review provides an overview of all methods and tools that have been published in the field of OCSR. Additionally, a small benchmark study was performed with the available open-source OCSR tools in order to examine their performance.

【 授权许可】

CC BY