An open source chemical structure curation pipeline using RDKit
By A. P. Bento, Anne Hersey, Eloy Felix, Gregory A. Landrum, Anna Gaulton, Francis Atkinson, L. Bellis, M. D. Veij, Andrew R. Leach. Journal of Cheminformatics, 2020
673 citations recorded by the source corpus.
Background The ChEMBL database is one of a number of public databases that contain bioactivity data on small molecule compounds curated from diverse sources. Incoming compounds are typically not standardised according to consistent rules. In order to maintain the quality of the final database and to easily compare and integrate data on the same compound from different sources it is necessary for the chemical structures in the database to be appropriately standardised. Results A chemical curation pipeline has been developed using the open source toolkit RDKit. It comprises three components: a Checker to test the validity of chemical structures and flag any serious errors; a Standardizer which formats compounds according to defined rules and conventions and a GetParent component that removes any salts and solvents from the compound to create its parent. This pipeline has been applied to the latest version of the ChEMBL database as well as uncurated datasets from other sources to test the robustness of the process and to identify common issues in database molecular structures. Conclusion All the components of the structure pipeline have been made freely available for other researchers to use and adapt for their own use. The code is available in a GitHub repository and it can also be accessed via the ChEMBL Beaker webservices. It has been used successfully to standardise the nearly 2 million compounds in the ChEMBL database and the compound validity checker has been used to identify compounds with the most serious issues so that they can be prioritised for manual curation.
Subfields: Cancer Research, Computational Theory and Mathematics, Pharmacology
View Paper: https://www.semanticscholar.org/paper/0956b0fa3f860992e3def8ee7ee7afacd4207405
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Citations in the corpus
- The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods (2023, methodology): “In collaboration with Dr Greg Landrum, we developed a new curation pipeline to standardise chemical structures in preparation for molecular modelling applications, described in Bento et al. ( 31 ).”
- QMugs, quantum mechanical properties of drug-like molecules (2022, methodology): “Molecules were neutralized, and salts and solvents were removed (“washing”) using the ChEMBL Structure Pipeline package 42 .”