title: Statistical analysis and compound selection of combinatorial libraries for soluble epoxide hydrolase.
abstract: 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.
authors: Structural and Computational Chemistry, Pfizer Global Research and Development, 700 Chesterfield Parkway West, Chesterfield, Missouri 63017, USA. li.xing@pfizer.com
authors: Li
authors: L
authors: Xing
authors: Robert
authors: R
authors: Goulet
authors: Kjell
authors: K
authors: Johnson