کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1396612 | 1501196 | 2009 | 7 صفحه PDF | دانلود رایگان |
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 IC50 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.
Gene expression programming as a novel machine learning technique is used to build nonlinear quantitative structure–activity relationship model for the prediction of the IC50 for the imidazopyridine anticoccidial compounds. 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.Figure optionsDownload as PowerPoint slide
Journal: European Journal of Medicinal Chemistry - Volume 44, Issue 10, October 2009, Pages 4044–4050