J Chem Inf Model - Statistical analysis and compound selection of combinatorial libraries for soluble epoxide hydrolase.

Tópicos

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Resumo

Inhibitors of soluble epoxide hydrolase (sEH) have been extensively pursued as antihypertensive therapies as well as potential treatment for other cardiovascular dysfunctions and prevention of renal damage. In this study we report quantitative structure-activity relationship (QSAR) models for 1223 structurally diverse sEH inhibitors produced by combinatorial library design and synthesis. Daylight fingerprints, MOE 2D and DragonX descriptors were generated for QSAR modeling approaches. Using these descriptors, a number of statistical models were trained and validated. Of these methods, gradient boosting machines (GBM), partial least-squares (PLS), and Cubist methods demonstrated the best performance on training and test set validation in terms of their leave-group-out cross-validated (LGO-CV) Q(2) and correlation coefficient R(2) (Q(2)(GBM-training) = 0.79, R(2)(GBM-test) = 0.81; Q(2)(PLS-training) = 0.75, R(2)(PLS-test) = 0.75; Q(2)(Cubist-training) = 0.91, R(2)(Cubist-test) = 0.78). A final model was constructed using the consensus approach of the three individual models and showed robust statistics and prediction of the external validation set. The Gaussian process modified sequential elimination of level combinations (G-SELC) method was then used to expand the chemical space beyond what has been explored by combinatorial synthesis. This approach identified 50 new compounds that are structurally diverse and potentially desirable for sEH inhibition based on prior knowledge. The activities of the suggested compounds were then predicted by the consensus QSAR model, and the results supported that the compounds were more likely to exist in the active parts of the chemical space. This study illustrates that the balanced approach by G-SELC could provide a general method for combinatorial library design, to effectively identify promising compounds to be created in the laboratory.

Resumo Limpo

inhibitor solubl epoxid hydrolas seh extens pursu antihypertens therapi well potenti treatment cardiovascular dysfunct prevent renal damag studi report quantit structureact relationship qsar model structur divers seh inhibitor produc combinatori librari design synthesi daylight fingerprint moe d dragonx descriptor generat qsar model approach use descriptor number statist model train valid method gradient boost machin gbm partial leastsquar pls cubist method demonstr best perform train test set valid term leavegroupout crossvalid lgocv q correl coeffici r qgbmtrain rgbmtest qplstrain rplstest qcubisttrain rcubisttest final model construct use consensus approach three individu model show robust statist predict extern valid set gaussian process modifi sequenti elimin level combin gselc method use expand chemic space beyond explor combinatori synthesi approach identifi new compound structur divers potenti desir seh inhibit base prior knowledg activ suggest compound predict consensus qsar model result support compound like exist activ part chemic space studi illustr balanc approach gselc provid general method combinatori librari design effect identifi promis compound creat laboratori

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