An algorithm to classify homologous series within compound datasets

Journal of Cheminformatics

Research

Christoph Steinbeck¹  Jonas Schaub¹  Emma L. Schymanski²  Adelene Lai³ 

[1] Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessing Strasse 8, 07743, Jena, Germany;Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg;Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg;Institute for Inorganic and Analytical Chemistry, Friedrich Schiller University Jena, Lessing Strasse 8, 07743, Jena, Germany;

关键词: RDKit;    Fragmentation;    Algorithm;    Scaffolds;    Homologous series;    Polymers;    Environmental chemistry;    Natural products;    Exposomics;    Pattern recognition;   

DOI  :  10.1186/s13321-022-00663-y

 received in 2022-08-31, accepted in 2022-11-27,  发布年份 2022

来源: Springer

【 摘 要 】

Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many homologous compounds remain unannotated as such in compound datasets, which poses obstacles to understanding chemical diversity and their analytical identification via database matching. To overcome these challenges, an algorithm to detect homologous series within compound datasets was developed and implemented using the RDKit. The algorithm takes a list of molecules as SMILES strings and a monomer (i.e., repeating unit) encoded as SMARTS as its main inputs. In an iterative process, substructure matching of repeating units, molecule fragmentation, and core detection lead to homologous series classification through grouping of identical cores. Three open compound datasets from environmental chemistry (NORMAN Suspect List Exchange, NORMAN-SLE), exposomics (PubChemLite for Exposomics), and natural products (the COlleCtion of Open NatUral producTs, COCONUT) were subject to homologous series classification using the algorithm. Over 2000, 12,000, and 5000 series with CH2 repeating units were classified in the NORMAN-SLE, PubChemLite, and COCONUT respectively. Validation of classified series was performed using published homologous series and structure categories, including a comparison with a similar existing method for categorising PFAS compounds. The OngLai algorithm and its implementation for classifying homologues are openly available at: https://github.com/adelenelai/onglai-classify-homologues.

【 授权许可】

CC BY   
© The Author(s) 2022

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