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.

Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW UK

Computational Drug Discovery Drug Discovery and Safety LACDR Leiden University Einsteinweg 55 2333 CC Leiden The Netherlands

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

CZ OPENSCREEN National Infrastructure for Chemical Biology Institute of Molecular Genetics of the ASCR v v i Vídeňská 1083 142 20 Prague 4 Czech Republic

Department of Mathematics Faculty of Chemical Engineering University of Chemistry and Technology Prague Technická 5 166 28 Prague Czech Republic

Helmholtz Zentrum Muenchen German Research Center for Environmental Health and BIGCHEM GmbH Ingolstaedter Landstrasse 1 85764 Neuherberg Germany

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Profiling and analysis of chemical compounds using pointwise mutual information

QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction