Приказ основних података о документу

dc.creatorAntanasijević, Davor
dc.creatorAntanasijević, Jelena
dc.creatorTrišović, Nemanja
dc.creatorUšćumlić, Gordana
dc.creatorPocajt, Viktor
dc.date.accessioned2021-03-10T13:25:48Z
dc.date.available2021-03-10T13:25:48Z
dc.date.issued2017
dc.identifier.issn1543-8384
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3614
dc.description.abstractSuccinimides, which contain a pharmacophore responsible for anticonvulsant activity, are frequently used antiepileptic drugs and the synthesis of their new derivatives with improved efficacy and tolerability presents an important task. Nowadays, multitarget/tasking methodologies focused on quantitative-structure activity relationships (mt-QSAR/mtk-QSAR) have an important role in the rational design of drugs since they enable simultaneous prediction of several standard measures of biological activities at diverse experimental conditions and against different biological targets. Relating to this very topic, the mt-QSAR/mtk-QSAR methodology can give only binary classification models, and as such, in this study a regression mtk-QSAR (rmtk-QSAR) model based on a novel modular neural network (MNN) has been proposed. The MNN uses standard classification mtk-QSAR models as input modules, while the regression is performed by the output module. The rmtk-QSAR model has been successfully developed for the simultaneous prediction of anticonvulsant activity and neurotoxicity of succinimides, with a satisfactory accuracy in testing (R-2 = 0.87). Thus, the proposed mtk-QSAR regression method can be regarded as a viable alternative to the standard QSAR methodology.en
dc.publisherAmer Chemical Soc, Washington
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172013/RS//
dc.rightsrestrictedAccess
dc.sourceMolecular Pharmaceutics
dc.subjectsuccinimidesen
dc.subjectmultitaskingen
dc.subjectQSARen
dc.subjectregressionen
dc.subjectmodular neural networken
dc.titleFrom Classification to Regression Multitasking QSAR Modeling Using a Novel Modular Neural Network: Simultaneous Prediction of Anticonvulsant Activity and Neurotoxicity of Succinimidesen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage4484
dc.citation.issue12
dc.citation.other14(12): 4476-4484
dc.citation.rankM21
dc.citation.spage4476
dc.citation.volume14
dc.identifier.doi10.1021/acs.molpharmaceut.7b00582
dc.identifier.pmid29130688
dc.identifier.scopus2-s2.0-85037633229
dc.identifier.wos000417342400034
dc.type.versionpublishedVersion


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Приказ основних података о документу