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Škuta C, Cortés-Ciriano I, Dehaen W, et al. QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. J Cheminform. 2020;12(1):39doi: 10.1186/s13321-020-00443-6.
Škuta, C., Cortés-Ciriano, I., Dehaen, W., Kříž, P., van Westen, G. J. P., Tetko, I. V., Bender, A., & Svozil, D. (2020). QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. Journal of cheminformatics, 12(1), 39. https://doi.org/10.1186/s13321-020-00443-6
Škuta, C, et al. "QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping." Journal of cheminformatics vol. 12,1 (2020): 39. doi: https://doi.org/10.1186/s13321-020-00443-6
Škuta C, Cortés-Ciriano I, Dehaen W, Kříž P, van Westen GJP, Tetko IV, Bender A, Svozil D. QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping. J Cheminform. 2020 May 29;12(1):39. doi: 10.1186/s13321-020-00443-6. PMID: 33431038; PMCID: PMC7260783.
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J Cheminform. 2020 May 29;12(1):39. doi: 10.1186/s13321-020-00443-6.

QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping.

Journal of cheminformatics

C Škuta, I Cortés-Ciriano, W Dehaen, P Kříž, G J P van Westen, I V Tetko, A Bender, D Svozil

Affiliations

  1. CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Víde?ská 1083, 142 20, Prague 4, Czech Republic.
  2. Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK.
  3. CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.
  4. Department of Mathematics, Faculty of Chemical Engineering, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic.
  5. Computational Drug Discovery, Drug Discovery and Safety, LACDR, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.
  6. Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH) and BIGCHEM GmbH, Ingolstaedter Landstrasse 1, 85764, Neuherberg, Germany.
  7. CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Víde?ská 1083, 142 20, Prague 4, Czech Republic. [email protected].
  8. CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28, Prague, Czech Republic. [email protected].

PMID: 33431038 PMCID: PMC7260783 DOI: 10.1186/s13321-020-00443-6

Abstract

An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.

Keywords: Affinity fingerprint; Bioactivity modeling; Biological fingerprint; QSAR; Scaffold hopping; Similarity searching

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