Comparative QSAR analyses of competitive CYP2C9 inhibitors using three-dimensional molecular descriptors

• Autor(a) principal: Lather, Viney
• Data de Publicação: 2011
• Outros Autores: Fernandes, Miguel X.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.13/5018
• topic: 3D-MoRSE; ADMET; CYP2C9; GETAWAY; QSAR; RDF; WHIM; .; Faculdade de Ciências Exatas e da Engenharia

One of the biggest challenges in QSAR studies using three-dimensional descriptors is to generate the bioactive conformation of the molecules. Com parative QSAR analyses have been performed on a dataset of 34 structurally diverse and competitive CYP2C9 inhibitors by generating their lowest energy conformers as well as additional multiple conformers for the calculation of molecular de scriptors. Three-dimensional descriptors account ing for the spatial characteristics of the molecules calculated using E-Dragon were used as the inde pendent variables. The robustness and the predic tive performance of the developed models were verified using both the internal [leave-one-out (LOO)] and external statistical validation (test set of 12 inhibitors). The best models (MLR using GET AWAY descriptors and partial least squares using 3D-MoRSE) were obtained by using the multiple conformers for the calculation of descriptors and were selected based upon the higher external pre diction (R2 test values of 0.65 and 0.63, respectively) and lower root mean square error of prediction (0.48 and 0.48, respectively). The predictive ability of the best model, i.e., MLR using GETAWAY de scriptors was additionally verified on an external test set of quinoline-4-carboxamide analogs and resulted in an R2 test value of 0.6. These simple and alignment-independent QSAR models offer the possibility to predict CYP2C9 inhibitory activity of chemically diverse ligands in the absence of X-ray crystallographic information of target protein structure and can provide useful insights about the ADMET properties of candidate molecules in the early phases of drug discovery.