Polley Mitchell J, Winkler David A, Burden Frank R
Centre for Complexity in Drug Design, CSIRO Molecular Science, Private Bag 10, Clayton South MDC, Clayton 3169, Australia.
J Med Chem. 2004 Dec 2;47(25):6230-8. doi: 10.1021/jm049621j.
Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.
法尼基转移酶抑制剂显示出作为新型抗癌药物的潜力。已知的抑制剂有很多，但构建预测性构效关系（SAR）模型的努力受到抑制剂结构多样性和灵活性的阻碍。我们首次对大量多样的法尼基转移酶抑制剂数据集的效力和选择性进行了定量构效关系（QSAR）研究。我们使用了基于分箱原子性质和分子矩阵不变量的新型分子描述符，以及一种强大的非线性QSAR映射范式——贝叶斯正则化神经网络。我们建立了法尼基转移酶抑制、香叶基香叶基转移酶抑制和体内数据的稳健QSAR模型。我们推导了一个新型选择性指数，使我们能够同时对效力和选择性进行建模，并使用该指数建立了稳健的QSAR模型，这些模型有潜力发现新的强效和选择性抑制剂。
