PARM: A genetic evolved algorithm to predict bioactivity

被引:45
作者
Chen, HM [1 ]
Zhou, JJ [1 ]
Xie, GR [1 ]
机构
[1] Chinese Acad Sci, Lab Comp Chem, Inst Chem Met, Beijing 100080, Peoples R China
来源
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES | 1998年 / 38卷 / 02期
关键词
D O I
10.1021/ci970004w
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Based on Waiters' GERM (Genetic Evolved Receptor Model) algorithm, an improved algorithm FARM (Pseudo Atomic Receptor Model) was put forward. PARM uses a combination of a genetic algorithm and a cross-validation technique to produce an atomic-level pseudoreceptor model, based on a set of known structure-activity relationships. During the genetic process, an artificial interfering method, which is based on a complementary principle of ligand-receptor interaction, was used to accelerate the search speed. The evolved models show a high correlation between intermolecular energy and bioactivity and can predict the bioactivity of an unknown molecule by interpolating in the regression equation of the structure-activity relationship. This algorithm was applied to two systems and produced reasonable results.
引用
收藏
页码:243 / 250
页数:8
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