基于外源化学物结构式和 PASS 预测算法的代谢部位预测。
Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm.
机构信息
Orekhovich Institute of Biomedical Chemistry of the Russian Academy of Medical Sciences , Building 10/8, Pogodinskaya Str., Moscow, 119121, Russia.
出版信息
J Chem Inf Model. 2014 Feb 24;54(2):498-507. doi: 10.1021/ci400472j. Epub 2014 Jan 17.
A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6.
一种新的基于配体的方法，用于预测外源物质代谢部位（SOMs），已基于 LMNA（标记多层次原子邻域）描述符和 PASS（物质活性谱预测）算法开发，并应用于预测细胞色素 P450 的 1A2、2C9、2C19、2D6 和 3A4 同工酶的 SOMs。通过留一法交叉验证程序计算的 SOMs 的平均 IAP（预测不变准确性）为 0.89。外部验证是使用包含 57 种心血管药物生物转化数据的评估集进行的。评估集的区域选择性的平均 IAP 为 0.83。结果表明，与 CYP 1A2、2D6、2C9、2C19 和 3A4 的 RS-Predictor 相比，所提出的方法提高了 SOM 预测的准确性，并且与 CYP 2C9 和 2D6 的 SMARTCyp 相当或更好。
Zotero 插件