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 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.

Graphical abstractGene 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 full-size imageDownload as PowerPoint slide