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Matrix‐based Molecular Descriptors for Prospective Virtual Compound Screening
Author(s) -
Grisoni Francesca,
Reker Daniel,
Schneider Petra,
Friedrich Lukas,
Consonni Viviana,
Todeschini Roberto,
Koeberle Andreas,
Werz Oliver,
Schneider Gisbert
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201600091
Subject(s) - virtual screening , pharmacophore , metric (unit) , similarity (geometry) , cheminformatics , computer science , molecular descriptor , matrix (chemical analysis) , data mining , pattern recognition (psychology) , artificial intelligence , machine learning , chemistry , quantitative structure–activity relationship , computational chemistry , stereochemistry , image (mathematics) , engineering , operations management , chromatography
Molecular descriptors capture diverse structural information of molecules and are a prerequisite for ligand‐based similarity searching. In this study, we introduce topological matrix‐based descriptors to virtual screening for hit discovery. We evaluated the usefulness of matrix‐based descriptors in a retrospective setting and compared them with topological pharmacophore descriptors. Special attention was given to the influence of data pre‐processing and the applied similarity metric on the virtual screening performance. Overall, the MB descriptors showed a competitive and complementary performance to other descriptors. A prospective screen of a commercial compound library led to the discovery of a novel natural‐product‐derived cyclooxygenase‐2 inhibitor predicted to interact differently with the target protein compared to the query compound ibuprofen. The results of our study motivate the use of matrix‐based descriptors for molecular similarity‐based virtual screening and scaffold hopping.