Senese Craig L, Hopfinger A J
Laboratory of Molecular Modeling and Design, Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy-University of Illinois at Chicago, 833 South Wood Street, Chicago, Illinois 60612-7231, USA.
J Chem Inf Comput Sci. 2003 Jul-Aug;43(4):1297-307. doi: 10.1021/ci0340456.
A training set of 27 norstatine derived inhibitors of HIV-1 protease, based on the 3(S)-amino-2(S)-hydroxyl-4-phenylbutanoic acid core (AHPBA), for which the -log IC(50) values were measured, was used to construct 4D-QSAR models. Five unique RI-4D-QSAR models, from two different alignments, were identified (q(2) = 0.86-0.95). These five models were used to map the atom type morphology of the lining of the inhibitor binding site at the HIV protease receptor site as well as predict the inhibition potencies of seven test set compounds for model validation. The five models, overall, predict the -log IC(50) activity values for the test set compounds in a manner consistent with their q(2) values. The models also correctly identify the hydrophobic nature of the HIV protease receptor site, and inferences are made as to further structural modifications to improve the potency of the AHPBA inhibitors of HIV protease. The finding of five unique, and nearly statistically equivalent, RI-4D-QSAR models for the training set demonstrates that there can be more than one way to fit structure-activity data even within a given QSAR methodology. This set of unique, equally good individual models is referred to as the manifold model.
基于3(S)-氨基-2(S)-羟基-4-苯基丁酸核心(AHPBA)的27种诺斯塔汀衍生的HIV-1蛋白酶抑制剂训练集,已测定其-log IC(50)值,用于构建4D-QSAR模型。从两种不同的比对中鉴定出五个独特的RI-4D-QSAR模型(q(2)=0.86-0.95)。这五个模型用于绘制HIV蛋白酶受体位点处抑制剂结合位点内衬的原子类型形态,以及预测七种测试集化合物的抑制效力以进行模型验证。总体而言,这五个模型以与其q(2)值一致的方式预测测试集化合物的-log IC(50)活性值。这些模型还正确地识别了HIV蛋白酶受体位点的疏水性质,并对进一步的结构修饰进行推断以提高AHPBA HIV蛋白酶抑制剂的效力。针对训练集发现了五个独特的、几乎在统计学上等效的RI-4D-QSAR模型,这表明即使在给定的QSAR方法内,拟合结构-活性数据也可能有不止一种方法。这组独特的、同样优秀的个体模型被称为流形模型。