Abstarct: In the first section of this project, Quantitative Structure-Activity Relationship (QSAR) has been developed for modeling and prediction of anti-trypanosomal activities of some dihydroquinolin compounds. The stepwise regression (SR) and genetic algorithm (GA) were used as variable selection methods. The selected variable by these methods was used as input of artificial neural network (ANN) to construct of model for prediction of anti-trypanosome activities of these compounds. The obtained models were validated by different techniques such as using the external test set, leave one out (LOO) and Y-randomization. The obtained results show that both models of SR-ANN and GA-ANN have a good prediction ability. The determination coefficient of the test set for SR-ANN and GA-ANN were obtained 0.9042, 0.8402 respectively. . . In the second part of this project, QSAR modeling for 42 derivatives of pyridopyridazine as P38 MAP kinase inhibitors was proposed. The descxriptors for building of model baxsed on ANN were selected by two methods of stepwise regression (SR) and genetic algorithm (GA). The determination coefficient of the test set for SR-ANN and GA-ANN were obtained 0.8607, 0.8027 respectively.