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|Title:||Prediction of factor Xa inhibitors by machine learning methods||Authors:||Lin, H.H.
Factor Xa (FXa)
Support vector machine (SVM)
|Issue Date:||Sep-2007||Citation:||Lin, H.H., Han, L.Y., Yap, C.W., Xue, Y., Liu, X.H., Zhu, F., Chen, Y.Z. (2007-09). Prediction of factor Xa inhibitors by machine learning methods. Journal of Molecular Graphics and Modelling 26 (2) : 505-518. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jmgm.2007.03.003||Abstract:||Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors. © 2007 Elsevier Inc. All rights reserved.||Source Title:||Journal of Molecular Graphics and Modelling||URI:||http://scholarbank.nus.edu.sg/handle/10635/106242||ISSN:||10933263||DOI:||10.1016/j.jmgm.2007.03.003|
|Appears in Collections:||Staff Publications|
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