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Titel: Differential Multimolecule Fingerprint for Similarity Search─Making Use of Active and Inactive Compound Sets in Virtual Screening
VerfasserIn: Hutter, Michael C.
Sprache: Englisch
Titel: Journal of Chemical Information and Modeling
Bandnummer: 62
Heft: 11
Seiten: 2726-2736
Verlag/Plattform: American Chemical Society (ACS)
Erscheinungsjahr: 2022
Freie Schlagwörter: Biological databases
Chemical specificity
Inhibitors
Mathematical methods
Molecules
DDC-Sachgruppe: 540 Chemie
Dokumenttyp: Journalartikel / Zeitschriftenartikel
Abstract: In conventional fingerprint methods, the similarity between two molecules is calculated using the Tanimoto index as a numerical criterion. Thus, the query molecules in virtual screening should be most representative of the wanted compound class at hand. In the concept introduced here, all available active molecules form a multimolecule fingerprint in which the appearing features are weighted according to their respective frequency. The features of inactive molecules are treated likewise and the resulting values are subtracted from those of the active ones. The obtained differential multimolecule fingerprint (DMMFP) is thus specific for the respective class of compounds. To account for the noninteger representation within this fingerprint, a modified Sørensen-Dice coefficient is used to compute the similarity. Potentially active molecules yield positive scores, whereas presumably inactive ones are denoted by negative values. The concept was applied to Angiotensin-converting enzyme (ACE) inhibitors, β2-adrenoceptor ligands, leukotriene A4 hydrolase inhibitors, dopamine D3 antagonists, and cytochrome CYP2C9 substrates, for which experimental binding affinities are known and was tested against decoys from DUD-E and a further background database consisting of molecules from the dark chemical matter, which comprises compounds that appear as frequent hitters across multiple assays. Using the 166 publicly available keys of the MACCS fingerprint and the larger PubChem fingerprint, actives were recovered with very high sensitivity. Furthermore, three marketed ACE inhibitors as well as the carbonic anhydrase II inhibitor dorzolamide were detected in the dark chemical matter data set. For comparison, the DMMFP was also used with a Bayesian classifier, for which the specificity (correctly classified inactives) and likewise the accuracy was superior. Conversely, the similarity score produced by the Sørensen-Dice coefficient showed its potential for the early recognition of (potentially) active molecules.
DOI der Erstveröffentlichung: 10.1021/acs.jcim.2c00242
URL der Erstveröffentlichung: https://pubs.acs.org/doi/10.1021/acs.jcim.2c00242
Link zu diesem Datensatz: urn:nbn:de:bsz:291--ds-400921
hdl:20.500.11880/36097
http://dx.doi.org/10.22028/D291-40092
ISSN: 1549-9596
Datum des Eintrags: 12-Jul-2023
Bezeichnung des in Beziehung stehenden Objekts: Supporting Information
In Beziehung stehendes Objekt: https://pubs.acs.org/doi/suppl/10.1021/acs.jcim.2c00242/suppl_file/ci2c00242_si_001.txt
Fakultät: NT - Naturwissenschaftlich- Technische Fakultät
Fachrichtung: NT - Biowissenschaften
Professur: NT - Prof. Dr. Volkhard Helms
Sammlung:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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