Please use this identifier to cite or link to this item: https://doi.org/10.1093/toxsci/kfh082
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dc.titlePrediction of torsade-causing potential of drugs by support vector machine approach
dc.contributor.authorYap, C.W.
dc.contributor.authorCai, C.Z.
dc.contributor.authorXue, Y.
dc.contributor.authorChen, Y.Z.
dc.date.accessioned2014-05-19T02:54:13Z
dc.date.available2014-05-19T02:54:13Z
dc.date.issued2004-05
dc.identifier.citationYap, 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
dc.identifier.issn10966080
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/53101
dc.description.abstractIn 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.
dc.sourceScopus
dc.subjectLinear solvation energy relationship
dc.subjectPrediction
dc.subjectSupport vector machine
dc.subjectTorsade de pointes
dc.typeArticle
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.contributor.departmentBIOPROCESSING TECHNOLOGY CENTRE
dc.contributor.departmentSINGAPORE-MIT ALLIANCE
dc.description.doi10.1093/toxsci/kfh082
dc.description.sourcetitleToxicological Sciences
dc.description.volume79
dc.description.issue1
dc.description.page170-177
dc.description.codenTOSCF
dc.identifier.isiut000221145900021
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