Prediction of Inhibition Activity of Dihydrofolate Reductase Inhibitors With Multivariate Adaptive Regression Splines
نویسندگان
چکیده
Dihydrofolate reductase (DHFR) enzyme is a crucial component of cell growth and proliferation in the human body, making it an important target for treating cancer diseases. This study aims to predict inhibitory activity (pXC50) dihydrofolate inhibitors terms quantitative structure-activity relationship (QSAR) model. Interpretation QSAR model vital understanding physicochemical processes assist structural optimisation. Multivariate adaptive regression splines (MARS), non-parametric technique, proposed non-linear between predictor variables response variable high-dimensional dataset. The dataset used this research consists pXC50 778 DHFR inhibitors. For our study, data divided into 80% training set building 20% testing validation. In comparison, baseline methods deep neural network (DNN) partial least squares (PLS) are also applied modeling. results show that MARS has best prediction accuracy according different measures, where RMSE, MAE, MAPE, RMSPE 0.96, 0.69, 0.11, 0.15 respectively. efficiency apparent its robust interaction variables, accuracy, ability overcome network’s black box system. Thus, technique can be considered excellent tool modeling datasets while exploring patterns data.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3272231