Please use this identifier to cite or link to this item:
|Title:||Prediction of torsade-causing potential of drugs by support vector machine approach||Authors:||Yap, C.W.
|Keywords:||Linear solvation energy relationship
Support vector machine
Torsade de pointes
|Issue Date:||May-2004||Citation:||Yap, C.W., Cai, C.Z., Xue, Y., Chen, Y.Z. (2004-05). Prediction of torsade-causing potential of drugs by support vector machine approach. Toxicological Sciences 79 (1) : 170-177. ScholarBank@NUS Repository. https://doi.org/10.1093/toxsci/kfh082||Abstract:||In an effort to facilitate drug discovery, computational methods for facilitating the prediction of various adverse drug reactions (ADRs) have been developed. So far, attention has not been sufficiently paid to the development of methods for the prediction of serious ADRs that occur less frequently. Some of these ADRs, such as torsade de pointes (TdP), are important issues in the approval of drugs for certain diseases. Thus there is a need to develop tools for facilitating the prediction of these ADRs. This work explores the use of a statistical learning method, support vector machine (SVM), for TdP prediction. TdP involves multiple mechanisms and SVM is a method suitable for such a problem. Our SVM classification system used a set of linear solvation energy relationship (LSER) descriptors and was optimized by leave-one-out cross validation procedure. Its prediction accuracy was evaluated by using an independent set of agents and by comparison with results obtained from other commonly used classification methods using the same dataset and optimization procedure. The accuracies for the SVM prediction of TdP-causing agents and non-TdP-causing agents are 97.4 and 84.6% respectively; one is substantially improved against and the other is comparable to the results obtained by other classification methods useful for multiple-mechanism prediction problems. This indicates the potential of SVM in facilitating the prediction of TdP-causing risk of small molecules and perhaps other ADRs that involve multiple mechanisms. © Society of Toxicology 2004; all rights reserved.||Source Title:||Toxicological Sciences||URI:||http://scholarbank.nus.edu.sg/handle/10635/53101||ISSN:||10966080||DOI:||10.1093/toxsci/kfh082|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jun 1, 2023
WEB OF SCIENCETM
checked on Jun 1, 2023
checked on May 25, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.