A DFT-based QSAR study on inhibition of human dihydrofolate reductase

Abstract

Diaminopyrimidine derivatives are frequently used as inhibitors of human dihydrofolate reductase, for example in treatment of patients whose immune system are affected by human immunodeficiency virus. Forty-seven dicyclic and tricyclic potential inhibitors of human dihydrofolate reductase were analyzed using the quantitative structure-activity analysis supported by DFT-based and DRAGON-based descriptors. The developed model yielded an RMSE deviation of 1.1 a correlation coefficient of 0.81. The prediction set was characterized by R=0.60 and RMSE=3.59. Factors responsible for inhibition process were identified and discussed. The resulting model was validated via cross validation and Y-scrambling procedure. From the best model, we found several mass-related descriptors and Sanderson electronegativity-related descriptors that have the best correlations with the investigated inhibitory concentration. These descriptors reflect results from QSAR studies based on characteristics of human dihydrofolate reductase inhibitors.

Synthesis, characterization, cytotoxic screening, and density functional theory studies of new derivatives of quinazolin-4(3H)-one Schiff bases

“…The QSAR has been used to determine the correlation between the physicochemical properties of various compounds and biological activities. This can predict the activities of new synthesized compounds( 26 27 ). Thus, the QSAR study of all studied compounds has been performed and presented in Table 5 .…”

QSAR and Classification Study on Prediction of Acute Oral Toxicity of N-Nitroso Compounds

“…Quantum chemistry calculations were prevalently used in the study of QSAR modeling [ 48 , 49 , 50 ]. The density functional theory (DFT) level of approximation for chemistry is suitable for many applications because of the better accuracy and the relative computational efficiency [ 51 , 52 , 53 ].…”

Predictive Modeling of the Human Hepatoma (Huh-7D12) Cancer Line of a Series of bis- (5-arylidene-rhodanine-3-yl) Diamine

“…RML and RMNL were generated using the XLSTAT software version 2016 [29] to predict the anticancer activity IC 50 . The equations of the different models were evaluated by the coefficient of determination (R 2 ) which measures the adequacy of the model and the predictive power of the QSAR model; the Root Mean Square Error (RMSE) which must be less than 10% of the range of the target property value [30]; the Fischer test (F) Test F, for the statistical significance of the model (higher is high, the better is the same set of descriptors and chemicals) [31] and the cross correlation coefficient (Q 2 CV ) which allows for evaluate the predictive power associated with a QSAR model ( > 0,6 for a satisfactory model while for an excellent model > 0,9 ) [32].…”