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Predicting targets of compounds against neurological diseases using cheminformatic methodology
dc.creator | Nikolić, Katarina | |
dc.creator | Mavridis, Lazaros | |
dc.creator | Bautista-Aguilera, Oscar M. | |
dc.creator | Marco-Contelles, Jose | |
dc.creator | Stark, Holger | |
dc.creator | Carreiras, Maria do Carmo | |
dc.creator | Rossi, Ilaria | |
dc.creator | Massarelli, Paola | |
dc.creator | Agbaba, Danica | |
dc.creator | Ramsay, Rona R. | |
dc.creator | Mitchell, John B. O. | |
dc.date.accessioned | 2019-09-02T11:46:51Z | |
dc.date.available | 2019-09-02T11:46:51Z | |
dc.date.issued | 2015 | |
dc.identifier.issn | 0920-654X | |
dc.identifier.uri | https://farfar.pharmacy.bg.ac.rs/handle/123456789/2395 | |
dc.description.abstract | Recently 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 H-3-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). | en |
dc.publisher | Springer, Dordrecht | |
dc.relation | EU COST Action STSM 10295 | |
dc.relation | Scottish Universities Life Sciences Alliance (SULSA) | |
dc.relation | Else Kroner-Fresenius-Stiftung | |
dc.relation | Translational Research Innovation-Pharma (TRIP) | |
dc.relation | Fraunhofer-Projektgruppe fur Translationale Medizin und Pharmakologie (TMP) | |
dc.rights | restrictedAccess | |
dc.source | Journal of Computer-Aided Molecular Design | |
dc.subject | Multi-targeted ligands | en |
dc.subject | Circular fingerprints | en |
dc.subject | Off-target study | en |
dc.subject | ChE | en |
dc.subject | MAO | en |
dc.subject | Histamine H3 receptor | en |
dc.subject | HMT | en |
dc.title | Predicting targets of compounds against neurological diseases using cheminformatic methodology | en |
dc.type | article | |
dc.rights.license | ARR | |
dcterms.abstract | Николић, Катарина; Марцо-Цонтеллес, Јосе; Митцхелл, Јохн Б. О.; Царреирас, Мариа до Цармо; Рамсаy, Рона Р.; Баутиста-Aгуилера, Осцар М.; Старк, Холгер; Мавридис, Лазарос; Aгбаба, Даница; Росси, Илариа; Массарелли, Паола; | |
dc.citation.volume | 29 | |
dc.citation.issue | 2 | |
dc.citation.spage | 183 | |
dc.citation.epage | 198 | |
dc.citation.other | 29(2): 183-198 | |
dc.citation.rank | M21 | |
dc.identifier.wos | 000348190700007 | |
dc.identifier.doi | 10.1007/s10822-014-9816-1 | |
dc.identifier.pmid | 25425329 | |
dc.identifier.scopus | 2-s2.0-84922105008 | |
dc.type.version | publishedVersion |