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PPARd é um receptor nuclear que, quando ativado, regula o metabolismo de carboidratos e lipídios, e está relacionado com diversas enfermidades, tais como síndrome metabólica e diabetes tipo 2. Para entender as principais interações entre alguns ligantes bioativos e o receptor PPARd, modelos de QSAR 2D e 3D foram obtidos e comparados com mapas de potencial eletrostático (MEP) e dos orbitais de fronteira (HOMO e LUMO), assim como resultados de docagem molecular. Os modelos de QSAR obtidos apresentaram bons resultados estatísticos e foram utilizados para predizer a atividade biológica de compostos do conjunto-teste (validação externa), e os valores preditos estão em concordância com os resultados experimentais. Além disso, todos mapas moleculares foram utilizados para avaliar as possíveis interações entre os ligantes e o receptor PPARd. Portanto, os modelos de QSAR 2D e 3D, assim como os mapas de HOMO, LUMO e MEP, podem fornecer informações sobre as principais propriedades necessárias para o planejamento de novos ligantes do receptor PPARd.PPARd is a nuclear receptor that, when activated, regulates the metabolism of carbohydrates and lipids and is related to metabolic syndrome and type 2 diabetes. To understand the main interactions between ligands and PPARd, we have constructed 2D and 3D QSAR models and compared them with HOMO, LUMO and electrostatic potential maps of the compounds studied, as well as docking results. All QSAR models showed good statistical parameters and prediction outcomes. The QSAR models were used to predict the biological activity of an external test set, and the predicted values are in good agreement with the experimental results. Furthermore, we employed all maps to evaluate the possible interactions between the ligands and PPARd. These predictive QSAR models, along with the HOMO, LUMO and MEP maps, can provide insights into the structural and chemical properties that are needed in the design of new PPARd ligands that have improved biological activity and can be employed to treat metabolic diseases.

MCH1R antagonists have been used to treat several diseases, such as obesity, depression and anxiety. In this study, we have performed several pharmacophore-based CoMFA studies for a series of 2,4,6 substituted quinolines as potent antagonists of MCH1R. Significant statistical results were obtained (q2 = 0.78, r2 = 0.99), indicating the high internal consistency of the 3D model generated, as well as its predictive power for untested compounds. The 3D model was externally validated employing a test set and the predicted biological values showed good agreement with experimental results. Important insights on the molecular interactions between the studied ligands and the MCH1R receptor, inferred from the 3D contour maps, were obtained and can be useful for the design of new structurally related analogs with improved binding affinity.

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