Nataraja Sekhar Y, Ravikumar Muttineni, Ravi Shashi Nayana M, Mallena Shyam C, Kishore Kumar Madala
Biocampus, S-1, Phase-1 Technocrats Industrial Estate, Balanagar, Hyderabad 500 037, Andra Pradesh, India.
Eur J Med Chem. 2008 May;43(5):1025-34. doi: 10.1016/j.ejmech.2007.06.024. Epub 2007 Jul 14.
Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed for 46 triazafluorenone derivatives, inhibiting metabotropic glutamate receptor subtype 1 (mGluR1). It includes molecular field analysis (MFA) and receptor surface analysis (RSA). The QSAR model was developed using 35 compounds and its predictive ability was assessed using a test set of 11 compounds. The predictive 3D-QSAR models have conventional r(2) values of 0.908 and 0.798 for MFA and RSA, respectively; while the cross-validated coefficient r(cv)(2) values of 0.707 and 0.580 for MFA and RSA, respectively. The results of 3D-QSAR methodologies provide a powerful tool directed to the design of novel and selective triazafluorenone inhibitors.
针对46种抑制代谢型谷氨酸受体1亚型（mGluR1）的三氮杂芴酮衍生物，构建了三维定量构效关系（3D-QSAR）模型。该模型包括分子场分析（MFA）和受体表面分析（RSA）。使用35种化合物构建QSAR模型，并使用11种化合物的测试集评估其预测能力。预测性3D-QSAR模型中，MFA和RSA的传统r(2)值分别为0.908和0.798；而MFA和RSA的交叉验证系数r(cv)(2)值分别为0.707和0.580。3D-QSAR方法的结果为新型选择性三氮杂芴酮抑制剂的设计提供了有力工具。
