Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.
Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.
Si
Hongzong
H
Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Qingdao, Shandong 266071, China. sihz03@126.com
Lian
Ning
N
Yuan
Shuping
S
Fu
Aiping
A
Duan
Yun-Bo
YB
Zhang
Kejun
K
Yao
Xiaojun
X
D000818:Animals; D000981:Antiprotozoal Agents / Q000737:chemistry* / Q000494:pharmacology*; D001185:Artificial Intelligence; D003044:Coccidia / Q000187:drug effects*; D003048:Coccidiosis / Q000188:drug therapy; D015195:Drug Design; D007093:Imidazoles / Q000737:chemistry* / Q000494:pharmacology*; D020128:Inhibitory Concentration 50; D008956:Models, Chemical; D011725:Pyridines / Q000737:chemistry* / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship
D000981:Antiprotozoal Agents; D007093:Imidazoles; D011725:Pyridines