2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors.

Alzheimer's disease (AD) accounts for almost three quarters of dementia patients and interferes people's normal life. Great progress has been made recently in the study of Acetylcholinesterase (AChE), known as one of AD's biomarkers. In this study, acetylcholinesterase inhibitors (AChEI) were collected to build a two-dimensional structure-activity relationship (2D-SAR) model and three-dimensional quantitative structure-activity relationship (3D-QSAR) model based on feature selection method combined with random forest. After calculation, the prediction accuracy of the 2D-SAR model was 89.63% by using the tenfold cross-validation test and 87.27% for the independent test set. Three cutting ways were employed to build 3D-QSAR models. A model with the highest [Formula: see text] (cross-validated correlation coefficient) and [Formula: see text](non-cross-validated correlation coefficient) was obtained to predict AChEI activity. The mean absolute error (MAE) of the training set and the test set was 0.0689 and 0.5273, respectively. In addition, molecular docking was also employed to reveal that the ionization state of the compounds had an impact upon their interaction with AChE. Molecular docking results indicate that Ser124 might be one of the active site residues.

2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors.

Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q2 = 0.784 and r2 = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs.