Title |
An algorithm to identify functional groups in organic molecules
|
---|---|
Published in |
Journal of Cheminformatics, June 2017
|
DOI | 10.1186/s13321-017-0225-z |
Pubmed ID | |
Authors |
Peter Ertl |
Abstract |
The concept of functional groups forms a basis of organic chemistry, medicinal chemistry, toxicity assessment, spectroscopy and also chemical nomenclature. All current software systems to identify functional groups are based on a predefined list of substructures. We are not aware of any program that can identify all functional groups in a molecule automatically. The algorithm presented in this article is an attempt to solve this scientific challenge. An algorithm to identify functional groups in a molecule based on iterative marching through its atoms is described. The procedure is illustrated by extracting functional groups from the bioactive portion of the ChEMBL database, resulting in identification of 3080 unique functional groups. A new algorithm to identify all functional groups in organic molecules is presented. The algorithm is relatively simple and full details with examples are provided, therefore implementation in any cheminformatics toolkit should be relatively easy. The new method allows the analysis of functional groups in large chemical databases in a way that was not possible using previous approaches. Graphical abstract . |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 17% |
United Kingdom | 3 | 8% |
Sweden | 2 | 6% |
France | 2 | 6% |
Switzerland | 1 | 3% |
Germany | 1 | 3% |
India | 1 | 3% |
Spain | 1 | 3% |
Canada | 1 | 3% |
Other | 1 | 3% |
Unknown | 17 | 47% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 20 | 56% |
Members of the public | 14 | 39% |
Science communicators (journalists, bloggers, editors) | 2 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
Germany | 1 | <1% |
Unknown | 215 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 39 | 18% |
Researcher | 39 | 18% |
Student > Master | 29 | 13% |
Student > Bachelor | 18 | 8% |
Other | 9 | 4% |
Other | 30 | 14% |
Unknown | 53 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 67 | 31% |
Biochemistry, Genetics and Molecular Biology | 22 | 10% |
Computer Science | 14 | 6% |
Pharmacology, Toxicology and Pharmaceutical Science | 11 | 5% |
Engineering | 7 | 3% |
Other | 37 | 17% |
Unknown | 59 | 27% |