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Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling

Abstract : Cyclooxygenases (COX) are present in the body in two isoforms, namely: COX-1, constitutively expressed, and COX-2, induced in physiopathological conditions such as cancer or chronic inflammation. The inhibition of COX with non-steroideal anti-inflammatory drugs (NSAIDs) is the most widely used treatment for chronic inflammation despite the adverse effects associated to prolonged NSAIDs intake. Although selective COX-2 inhibition has been shown not to palliate all adverse effects (e.g. cardiotoxicity), there are still niche populations which can benefit from selective COX-2 inhibition. Thus, capitalizing on bioactivity data from both isoforms simultaneously would contribute to develop COX inhibitors with better safety profiles. We applied ensemble proteochemometric modeling (PCM) for the prediction of the potency of 3,228 distinct COX inhibitors on 11 mammalian cyclooxygenases. Ensemble PCM models (R 2 0 test = 0.65, and RMSE test = 0.71) outperformed models exclusively trained on compound (R 2 0 test = 0.17, and RMSE test = 1.09) or protein descriptors (R 2 0 test = 0.16 and RMSE test = 1.10) on the test set. Moreover, PCM predicted COX potency for 1,086 selective and non-selective COX inhibitors with R 2 0 test = 0.59 and RMSE test = 0.76. These values are in agreement with the maximum and minimum achievable R 2 0 test and RMSE test values of approximately 0.68 for both metrics. Confidence intervals for individual predictions were calculated from the standard deviation of the predictions from the individual models composing the ensembles. Finally, two substructure analysis pipelines singled out chemical substructures implicated in both potency and selectivity in agreement with the literature.
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Isidro Cortes-Ciriano, Daniel S Murrell, Gerard van Westen, Andreas Bender, Thérèse E Malliavin. Prediction of the potency of mammalian cyclooxygenase inhibitors with ensemble proteochemometric modeling. Journal of Cheminformatics, 2015, 7 (1), pp.2518 - 2525. ⟨10.1093/bioinformatics/btn479⟩. ⟨pasteur-01398459⟩



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