致癌物
毒性
化学
集合(抽象数据类型)
数量结构-活动关系
计算机科学
立体化学
有机化学
程序设计语言
作者
Qi Liao,Jiahao Yao,F Li,Shuofeng Yuan,J. P. Doucet,Annick Panaye,Baoyan Fan
标识
DOI:10.1080/10629360410001697780
摘要
A SAR based carcinogenic toxicity prediction system, CISOC-PSCT, was developed. It consisted of two principal phases: the construction of relationships between structural descriptors and carcinogenic toxicity indices, and prediction of the toxicity from the SAR model. The training set included 2738 carcinogenic and 4130 non-carcinogenic compounds. Three predefined topological types of substructures termed Star, Path and Ring were used to generate the descriptors for each structure in the training set. In this system, the defined carcinogenic toxicity index (CTI) was obtained from the probability of a structural descriptor to either belong to the carcinogenic or non-carcinogenic compounds. Based on these structural descriptors and their CTI, a SAR model was derived. Then the carcinogenic possibility (CP) and the carcinogenic impossibility (CIP) of compounds were predicted. The model was tested from a testing set of 304 carcinogenic compounds (MDL toxicity database), 460 non-carcinogenic compounds (CMC database) and 94 compounds extracted from two traditional Chinese medicine herbs.
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