From the scientific community, a lot of effort has been spent on the correct identification of gene and protein names in text, while less effort has been spent on the correct identification of chemical names. Dictionary-based term identification has the power to recognize the diverse representation of chemical information in the literature and map the chemicals to their database identifiers. We developed a dictionary for the identification of small molecules and drugs in text, combining information from UMLS, MeSH, ChEBI, DrugBank, KEGG, HMDB, and ChemIDplus. Rule-based term filtering, manual check of highly frequent terms, and disambiguation rules were applied. We tested the combined dictionary and the dictionaries derived from the individual resources on an annotated corpus, and conclude the following: (1) each of the different processing steps increase precision with a minor loss of recall; (2) the overall performance of the combined dictionary is acceptable (precision 0.67, recall 0.40 (0.80 for trivial names); (3) the combined dictionary performed better than the dictionary in the chemical recognizer OSCAR3; (4) the performance of a dictionary based on ChemIDplus alone is comparable to the performance of the combined dictionary.
List of database identifier names:
CHID = ChemIDplus.
CHEB = ChEBI
CAS = CAS number
PUBC = PubChem compound
PUBS = PubChem substance
INCH = InChI string
DRUG = DrugBank
HMBD = Human Metabolome Database
KEGG = KEGG compound
KEGD = KEGG drug
MESH = Medical Subject Headings (and supplemental records)
If you have used Jochem in your study, please cite:
Kristina M. Hettne, Rob H. Stierum, Martijn J. Schuemie, Peter J. M. Hendriksen, Bob J. A. Schijvenaars, Erik M. van Mulligen, Jos Kleinjans, and Jan A. Kors. (2009) A Dictionary to Identify Small Molecules and Drugs in Free Text. Bioinformatics. 2009:25(22):2983-91