从分子结构预测生物转化潜力。

Predicting biotransformation potential from molecular structure.

作者信息

Borodina Yu, Sadym A, Filimonov D, Blinova V, Dmitriev A, Poroikov V

机构信息

Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry of Russian Academy of Medical Sciences, 10 Pogodinskaya Str, Moscow 119121, Russia.

出版信息

J Chem Inf Comput Sci. 2003 Sep-Oct;43(5):1636-46. doi: 10.1021/ci034078l.

DOI:10.1021/ci034078l

PMID:14502498

Abstract

The program PASS-BioTransfo is presented, which is capable of predicting many classes of biotransformation for chemical compounds. A particular class of biotransformation is defined by the chemical transformation type and may additionally include the name of the enzyme involved in a transformation. An evaluation of the approach is presented, using biotransformations taken from the databases Metabolite (MDL) and Metabolism (Accelrys), respectively. When trained with biotransformations from Metabolite, PASS-BioTransfo predicts 1927 classes of biotransformation; the average accuracy estimated in LOO cross-validation is about 88%. After training with the biotransformations from the Metabolism database, 178 classes of biotransformation are predicted with an average accuracy of about 85%. The results of cross-prediction with several training and evaluation sets are presented and discussed.

摘要

介绍了程序PASS-BioTransfo，它能够预测化合物的多种生物转化类别。特定的生物转化类别由化学转化类型定义，并且可能另外包括参与转化的酶的名称。分别使用取自Metabolite（MDL）数据库和Metabolism（Accelrys）数据库的生物转化数据对该方法进行了评估。当使用来自Metabolite的生物转化数据进行训练时，PASS-BioTransfo预测了1927种生物转化类别；在留一法交叉验证中估计的平均准确率约为88%。使用来自Metabolism数据库的生物转化数据进行训练后，预测了178种生物转化类别，平均准确率约为85%。展示并讨论了使用多个训练集和评估集进行交叉预测的结果。