Information Retrieval and Text Mining Technologies for Chemistry

Abstract

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

A Critical Review of Machine Learning of Energy Materials

“…Furthermore, the vast majority of databases are commercial products requiring a license, and programmatic application programming interfaces (APIs) for large‐scale data access are rarely implemented. A large fraction of experimental data are only available in journal publications, though recent successes in text mining offer a potential solution to this conundrum . Finally, major efforts are underway in high‐throughput/combinatorial experiments that can generate large experimental materials database with diverse properties …”

“…A large fraction of experimental data are only available in journal publications, though recent successes in text mining offer a potential solution to this conundrum. [48][49][50][51][52][53] Finally, major efforts are underway in high-throughput/combinatorial experiments that can generate large experimental materials database with diverse properties. [31] It is therefore not surprising that many ML works have turned to computed data sources.…”

Symbolic regression in materials science

“…With recent advances in computer science, theoretical modelling, and experimental instrumentation, materials scientists have in many ways created a "mechanical Brahe" and marched into a new era of big data. Datasets of materials information, obtained from advanced characterization techniques, 1-3 combinatorial experiments, [4][5][6] high-throughput first-principles simulations, 7,8 literature mining, 9,10 and other techniques, are created at a faster rate every day with less and less human labor. All of this data enables new opportunities to construct novel laws of phenomenological behavior for systems that previously lacked them.…”

A novel deep learning method for extracting unspecific biomedical relation

“…We used the ChemProt corpus provided by the task organizer, 26 which consists of 4966 PubMed abstracts and 126,457 annotated chemical and protein entities. These relations annotated 10 chemical-protein relationships.…”