Prediction of inhibition constants of (R)-3-amidinophenylalanine inhibitors toward factor Xa by 2D-QSAR model

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Thi Bich Van Pham Minh Hao Hoang

Abstract

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. (R)-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 (R)-3-amidinophenylalanine inhibitors (the training set) with respect to their pKi values toward factor Xa, where pKi=-logKi, and Ki 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 pKi values. The statistically significant parameters of the model, such as squared correlation coefficient, R2=0.834, root mean square error, RMSE=0.210, cross-validated Q2cv=0.789, and cross-validated RMSEcv=0.237, were obtained for the training set. The developed 2D-QSAR model was applied to predict the pKi 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.

DOI: https://doi.org/10.31276/VJSTE.62(2).24-29

Article Details

How to Cite
PHAM, Thi Bich Van; HOANG, Minh Hao. Prediction of inhibition constants of (R)-3-amidinophenylalanine inhibitors toward factor Xa by 2D-QSAR model. Vietnam Journal of Science, Technology and Engineering, [S.l.], v. 62, n. 2, p. 24-29, july 2020. ISSN 2525-2461. Available at: <https://vietnamscience.vjst.vn/index.php/VJSTE/article/view/316>. Date accessed: 28 sep. 2020.
Section
PHYSICAL SCIENCES