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- Title
Predicting targets of compounds against neurological diseases using cheminformatic methodology.
- Authors
Nikolic, Katarina; Mavridis, Lazaros; Bautista-Aguilera, Oscar; Marco-Contelles, José; Stark, Holger; Carmo Carreiras, Maria; Rossi, Ilaria; Massarelli, Paola; Agbaba, Danica; Ramsay, Rona; Mitchell, John
- 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-receptor (HR). 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 (HR/DR/DR/5-HTR) models and by in vitro assays for serotonin 5-HT and 5-HT receptor binding of the most promising ligand ( 71/MBA-VEG8).
- Subjects
TARGETED drug delivery; CHEMICALS; NEUROLOGY; NEUROLOGICAL disorders; CHEMINFORMATICS; MONOAMINE oxidase
- Publication
Journal of Computer-Aided Molecular Design, 2015, Vol 29, Issue 2, p183
- ISSN
0920-654X
- Publication type
Article
- DOI
10.1007/s10822-014-9816-1