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dc.contributor.authorNikolic, Katarina
dc.contributor.authorMavridis, Lazaros
dc.contributor.authorBautista-Aguilera, Oscar M.
dc.contributor.authorMarco-Contelles, Jose
dc.contributor.authorStark, Holger
dc.contributor.authorCarreiras, Maria do Carmo
dc.contributor.authorRossi, Ilaria
dc.contributor.authorMassarelli, Paola
dc.contributor.authorAgbaba, Danica
dc.contributor.authorRamsay, Rona R.
dc.contributor.authorMitchell, John B. O.
dc.date.accessioned2015-11-26T00:11:56Z
dc.date.available2015-11-26T00:11:56Z
dc.date.issued2015-02
dc.identifier.citationNikolic , K , Mavridis , L , Bautista-Aguilera , O M , Marco-Contelles , J , Stark , H , Carreiras , M D C , Rossi , I , Massarelli , P , Agbaba , D , Ramsay , R R & Mitchell , J B O 2015 , ' Predicting targets of compounds against neurological diseases using cheminformatic methodology ' Journal of Computer-Aided Molecular Design , vol. 29 , no. 2 , pp. 183-198 . https://doi.org/10.1007/s10822-014-9816-1en
dc.identifier.issn0920-654X
dc.identifier.otherPURE: 158737015
dc.identifier.otherPURE UUID: d8f6ca5d-4d4f-43d3-a0b9-df2e143777c2
dc.identifier.otherScopus: 84922105008
dc.identifier.otherWOS: 000348190700007
dc.identifier.otherPubMed: 25425329
dc.identifier.otherORCID: /0000-0003-1535-4904/work/34907347
dc.identifier.otherORCID: /0000-0002-0379-6097/work/34033388
dc.identifier.urihttp://hdl.handle.net/10023/7849
dc.descriptionThe authors acknowledge financial support from the Scottish Universities Life Sciences Alliance (SULSA). OMBA and JMC thank MINECO (Spain) for a fellowship, and support (SAF2012-33304), respectively. KN and DA acknowledge project supported by the Ministry of Education and Science of the Republic of Serbia, Contract No. 172033. Further supports by Else Kroner-Fresenius-Stiftung, Translational Research Innovation—Pharma (TRIP), Fraunhofer-Projektgruppe fur Translationale Medizin und Pharmakologie (TMP) (to HS) and the European COST Actions BM1007, CM1103 (including STSM 10295 to KN), and CM1207 are also gratefully acknowledged.en
dc.description.abstractRecently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer’s disease, obsessive disorders, and Parkinson’s disease. A probabilistic method, the Parzen–Rosenblatt window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a “predictor” model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand (71/MBA-VEG8).
dc.format.extent16
dc.language.isoeng
dc.relation.ispartofJournal of Computer-Aided Molecular Designen
dc.rightsCopyright 2014. Springer International Publishing Switzerland. The final publication is available at Springer via http://dx.doi.org/10.1007/s10822-014-9816-1en
dc.subjectMulti-targeted ligandsen
dc.subjectCircular fingerprintsen
dc.subjectOff-target studyen
dc.subjectChEen
dc.subjectMAOen
dc.subjectHistamine H3 receptoren
dc.subjectHMTen
dc.subjectQR Microbiologyen
dc.subjectQA76 Computer softwareen
dc.subjectR Medicine (General)en
dc.subject.lccQRen
dc.subject.lccQA76en
dc.subject.lccR1en
dc.titlePredicting targets of compounds against neurological diseases using cheminformatic methodologyen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Chemistryen
dc.contributor.institutionUniversity of St Andrews.Biomedical Sciences Research Complexen
dc.contributor.institutionUniversity of St Andrews.EaSTCHEMen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.identifier.doihttps://doi.org/10.1007/s10822-014-9816-1
dc.description.statusPeer revieweden
dc.date.embargoedUntil2016-02-01


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