Please use this identifier to cite or link to this item: https://doi.org/10.2174/138955706776361501
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dc.titlePrediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods
dc.contributor.authorYap, C.W.
dc.contributor.authorXue, Y.
dc.contributor.authorLi, H.
dc.contributor.authorLi, Z.R.
dc.contributor.authorUng, C.Y.
dc.contributor.authorHan, L.Y.
dc.contributor.authorZheng, C.J.
dc.contributor.authorCao, Z.W.
dc.contributor.authorChen, Y.Z.
dc.date.accessioned2014-06-23T05:57:30Z
dc.date.available2014-06-23T05:57:30Z
dc.date.issued2006-04
dc.identifier.citationYap, C.W., Xue, Y., Li, H., Li, Z.R., Ung, C.Y., Han, L.Y., Zheng, C.J., Cao, Z.W., Chen, Y.Z. (2006-04). Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. Mini-Reviews in Medicinal Chemistry 6 (4) : 449-459. ScholarBank@NUS Repository. https://doi.org/10.2174/138955706776361501
dc.identifier.issn13895575
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/77632
dc.description.abstractComputational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds. © 2006 Bentham Science Publishers Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.2174/138955706776361501
dc.sourceScopus
dc.subjectMolecular descriptors
dc.subjectPharmacodynamic
dc.subjectPharmacokinetic
dc.subjectQSAR
dc.subjectQSPR
dc.subjectStatistical learning methods
dc.subjectStructural diversity
dc.subjectToxicology
dc.typeReview
dc.contributor.departmentPHARMACY
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.contributor.departmentBIOCHEMISTRY
dc.description.doi10.2174/138955706776361501
dc.description.sourcetitleMini-Reviews in Medicinal Chemistry
dc.description.volume6
dc.description.issue4
dc.description.page449-459
dc.description.codenMMCIA
dc.identifier.isiut000236267700009
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