Diabetes is a serious, long-term (or chronic) disease that occurs when a person’s blood sugar levels are high because their body cannot produce enough insulin, or does not produce enough insulin or that it cannot eff...Diabetes is a serious, long-term (or chronic) disease that occurs when a person’s blood sugar levels are high because their body cannot produce enough insulin, or does not produce enough insulin or that it cannot effectively use the insulin it produces. According to the literature, this disease has several causes, but certain types of diabetes such as type 2 diabetes are most closely linked to a metabolic disorder due to abdominal obesity. Thus, the number of individuals with type 2 diabetes is increasing. It is with this in mind that we work to improve human health. The aim of this study is to design new derivatives of 1,3,4-thiadiazole with improved antidiabetic activity by the mathematical model of multiple linear regression (MLR) established previously. The analysis of the effect on the substituents influencing the antidiabetic activity, fourteen (14) new molecules coded CDTH were generated and presenting values of the potential of inhibitory concentration higher than that of the base compound (pIC50 = 2.526). But thirteen (13) of these new compounds belong to the domain of applicability of the MLR model established previously. In addition, the thermodynamic quantities of formation formed at 298K have been calculated. Lipinski’s rule and pharmacokinetic properties proved that five (5) (TH4, TH9, TH10, TH13 and TH14) new molecules can be used as diabetes medicine.展开更多
This work was carried out on a series of twenty-two (22) benzimidazole derivatives with inhibitory activities against Mycobacterium tuberculosis H37Rv by applying the Quantitative Structure-Activity Relationship (QSAR...This work was carried out on a series of twenty-two (22) benzimidazole derivatives with inhibitory activities against Mycobacterium tuberculosis H37Rv by applying the Quantitative Structure-Activity Relationship (QSAR) method. The molecules were optimized at the level DFT/B3LYP/6-31 + G (d, p), to obtain the molecular descriptors. We used three statistical learning tools namely, the linear multiple regression (LMR) method, the nonlinear regression (NLMR) and the artificial neural network (ANN) method. These methods allowed us to obtain three (3) quantitative models from the quantum descriptors that are, chemical potential (μ), polarizability (α), bond length l (C = N), and lipophilicity. These models showed good statistical performance. Among these, the ANN has a significantly better predictive ability R<sup>2</sup> = 0.9995;RMSE = 0.0149;F = 31879.0548. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the internal validation tests show that the model has a very satisfactory internal predictive character and can be considered as robust. Moreover, the applicability range of this model determined from the levers shows that a prediction of the pMIC of the new benzimidazole derivatives is acceptable when its lever value is lower than 1.展开更多
文摘Diabetes is a serious, long-term (or chronic) disease that occurs when a person’s blood sugar levels are high because their body cannot produce enough insulin, or does not produce enough insulin or that it cannot effectively use the insulin it produces. According to the literature, this disease has several causes, but certain types of diabetes such as type 2 diabetes are most closely linked to a metabolic disorder due to abdominal obesity. Thus, the number of individuals with type 2 diabetes is increasing. It is with this in mind that we work to improve human health. The aim of this study is to design new derivatives of 1,3,4-thiadiazole with improved antidiabetic activity by the mathematical model of multiple linear regression (MLR) established previously. The analysis of the effect on the substituents influencing the antidiabetic activity, fourteen (14) new molecules coded CDTH were generated and presenting values of the potential of inhibitory concentration higher than that of the base compound (pIC50 = 2.526). But thirteen (13) of these new compounds belong to the domain of applicability of the MLR model established previously. In addition, the thermodynamic quantities of formation formed at 298K have been calculated. Lipinski’s rule and pharmacokinetic properties proved that five (5) (TH4, TH9, TH10, TH13 and TH14) new molecules can be used as diabetes medicine.
文摘This work was carried out on a series of twenty-two (22) benzimidazole derivatives with inhibitory activities against Mycobacterium tuberculosis H37Rv by applying the Quantitative Structure-Activity Relationship (QSAR) method. The molecules were optimized at the level DFT/B3LYP/6-31 + G (d, p), to obtain the molecular descriptors. We used three statistical learning tools namely, the linear multiple regression (LMR) method, the nonlinear regression (NLMR) and the artificial neural network (ANN) method. These methods allowed us to obtain three (3) quantitative models from the quantum descriptors that are, chemical potential (μ), polarizability (α), bond length l (C = N), and lipophilicity. These models showed good statistical performance. Among these, the ANN has a significantly better predictive ability R<sup>2</sup> = 0.9995;RMSE = 0.0149;F = 31879.0548. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the internal validation tests show that the model has a very satisfactory internal predictive character and can be considered as robust. Moreover, the applicability range of this model determined from the levers shows that a prediction of the pMIC of the new benzimidazole derivatives is acceptable when its lever value is lower than 1.
