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

Funder: FP7 People: Marie-Curie Actions; doi: http://dx.doi.org/10.13039/100011264; Grant(s): 238701, 238701An 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

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

Medicinal Chemistr

Modifying Rap1-signalling by targeting Pde6δ is neuroprotective in models of Alzheimer’s disease

Background: Neuronal Ca2+ dyshomeostasis and hyperactivity play a central role in Alzheimer's disease pathology arid progression. Amyloid-beta together with non-genetic risk-factors of Alzheimer's disease contributes to increased Ca2+ influx and aberrant neuronal activity, which accelerates neurodegeneration in a feed-forward fashion. As such, identifying new targets and drugs to modulate excessive Ca2+ signalling and neuronal hyperactivity, without overly suppressing them, has promising therapeutic potential. Methods: Here we show, using biochemical, electrophysiological, imaging, and behavioural tools, that pharmacological modulation of Rap1 signalling by inhibiting its interaction with Pde6 delta normalises disease associated Ca2+ aberrations and neuronal activity, conferring neuroprotection in models of Alzheimer's disease. Results: The newly identified inhibitors of the Rap1-Pde6 delta interaction counteract AD phenotypes, by reconfiguring Rapt signalling underlying synaptic efficacy, Ca2+ influx, and neuronal repolarisation, without adverse effects in-cellulo or invivo. Thus, modulation of Rap1 by Pde6 delta accommodates key mechanisms underlying neuronal activity, and therefore represents a promising new drug target for early or late intervention in neurodegenerative disorders. Conclusion: Targeting the Pde6 delta-Rap1 interaction has promising therapeutic potential for disorders characterised by neuronal hyperactivity, such as Alzheimer's disease

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

Funder: FP7 People: Marie-Curie Actions; doi: http://dx.doi.org/10.13039/100011264; Grant(s): 238701, 238701Abstract: 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

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

Funder: FP7 People: Marie-Curie Actions; doi: http://dx.doi.org/10.13039/100011264; Grant(s): 238701, 238701Abstract: 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

Design of Group IIA Secreted/Synovial Phospholipase A2 Inhibitors: An Oxadiazolone Derivative Suppresses Chondrocyte Prostaglandin E2 Secretion

Group IIA secreted/synovial phospholipase A2 (GIIAPLA2) is an enzyme involved in the synthesis of eicosanoids such as prostaglandin E2 (PGE2), the main eicosanoid contributing to pain and inflammation in rheumatic diseases. We designed, by molecular modeling, 7 novel analogs of 3-{4-[5(indol-1-yl)pentoxy]benzyl}-4H-1,2,4-oxadiazol-5-one, denoted C1, an inhibitor of the GIIAPLA2 enzyme. We report the results of molecular dynamics studies of the complexes between these derivatives and GIIAPLA2, along with their chemical synthesis and results from PLA2 inhibition tests. Modeling predicted some derivatives to display greater GIIAPLA2 affinities than did C1, and such predictions were confirmed by in vitro PLA2 enzymatic tests. Compound C8, endowed with the most favorable energy balance, was shown experimentally to be the strongest GIIAPLA2 inhibitor. Moreover, it displayed an anti-inflammatory activity on rabbit articular chondrocytes, as shown by its capacity to inhibit IL-1β-stimulated PGE2 secretion in these cells. Interestingly, it did not modify the COX-1 to COX-2 ratio. C8 is therefore a potential candidate for anti-inflammatory therapy in joints