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

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 Mor-gan2 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.

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

“…Given the substantial diversity in performance of the 1360 base models used to generate QAFFP [29], we next sought to investigate whether we could better model the 18 cytotoxicity data sets by computing rv-QAFFP using only those base models showing high predictive power. To this end, we used increasingly higher cut-off values for the minimum R 2 test value a base model needs to show to be considered for the calculation of rv-QAFFP.…”

“…The protocol to calculate QSAR-based affinity fingerprints using ChEMBL data is explained in detail in the accompanying manuscript [29]. In brief, the workflow can be summarized in the following five steps ( Fig.…”

“…In the following and in the accompanying manuscript, we use the term affinity fingerprint to refer to the set of biological endpoints, experimentally determined or predicted, irrespective of whether the endpoint measured corresponds to a potency or an affinity metric. For a comprehensive review of existing methods to predict affinity fingerprints using existing high-throughput data [16][17][18][19][20][21][22][23][24][25][26][27][28], the reader is referred to the introduction of the accompanying manuscript [29].…”

“…The construction of such in silico affinity fingerprints, termed QSAR-derived Quantitative Affinity Fingerprints (QAFFPs), is described in the accompanying manuscript [29] where their performance for similarity searching, compound activity classification and scaffold hopping is reported. The range of application of QAFFP is further enhanced in the present manuscript, in which the use of QAFFPs in regression settings is studied.…”

Profiling and analysis of chemical compounds using pointwise mutual information

“…with the aim to improve the interpretability of the resulting interrelation profiles. Later, we will also investigate the utility of hybrid feature vectors containing interrelation profiles concatenated with, for example, QAFFP biological fingerprints [64,65] or with other features of interest. We plan to use interrelation profiling in various cheminformatics applications, such as in biological activity classification or potency prediction, focused chemical library construction, diversity data selection or ensemble modeling using RFT together with domain-specific models for, e.g., natural product likeness assessment [66][67][68].…”