Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.
Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified.
Hou
T J
TJ
Department of Chemistry, Peking University, Beijing, People's Republic of China.
Wang
J M
JM
Liao
N
N
Xu
X J
XJ
D000465:Algorithms; D000818:Animals; D000927:Anticonvulsants / Q000737:chemistry* / Q000494:pharmacology*; D002934:Cinnamates / Q000737:chemistry* / Q000494:pharmacology*; D016014:Linear Models; D051379:Mice; D008957:Models, Genetic; D015394:Molecular Structure; D012640:Seizures / Q000188:drug therapy; D013329:Structure-Activity Relationship
D000927:Anticonvulsants; D002934:Cinnamates