TY - JOUR
AU - Thi Bich Van Pham,
AU - Minh Hao Hoang,
PY - 2020/06/15
Y2 - 2024/09/13
TI - Prediction of inhibition constants of (R)-3-amidinophenylalanine inhibitors toward factor Xa by 2D-QSAR model
JF - Vietnam Journal of Science, Technology and Engineering
JA - VJSTE
VL - 62
IS - 2
SE -
DO - 10.31276/VJSTE.62(2).24-29
UR - https://vietnamscience.vjst.vn/index.php/vjste/article/view/122
SP - 24-29
AB - <p>A coagulation cascade forms through proteolytic reactions and involves different factors. There are two coagulation pathways, including intrinsic and extrinsic mechanisms, which converge by the formation of factor Xa. Factor Xa plays a crucial role in the formation of the complex with factor Va in the presence of calcium ions and phospholipids. This complex converts prothrombin to thrombin, which leads to the formation of a very strong fibrin clot. Much effort has been devoted to the efficient interference of this enzyme cascade by the inhibition of factor Xa due to its important effect. (<em>R</em>)-3-amidinophenylalanine inhibitors are known inhibitors of factor Xa reported so far. In the present work, a two-dimensional quantitative structure activity relationship (2D-QSAR) was performed on 50 (<em>R</em>)-3-amidinophenylalanine inhibitors (the training set) with respect to their pKi values toward factor Xa, where pK<sub>i</sub>=-logKi, and K<sub>i</sub> is the inhibition constant, to develop a mathematical model that depends on the physicochemical properties of the inhibitors. Partial least squares regression (PLSR) was used to yield a QSAR model containing molecular descriptors that significantly contribute to pK<sub>i</sub> values. The statistically significant parameters of the model, such as squared correlation coefficient, R<sup>2</sup>=0.834, root mean square error, RMSE=0.210, cross-validated Q<sup>2</sup><sub>cv</sub>=0.789, and cross-validated RMSE<sub>cv</sub>=0.237, were obtained for the training set. The developed 2D-QSAR model was applied to predict the pK<sub>i</sub> values of the 62 inhibitors. Furthermore, the reliability of the model was also confirmed via statistically significant parameters obtained from validation on an external set.</p>
ER -