Title
Predicting targets of compounds against neurological diseases using cheminformatic methodology.
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 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).
Year
DOI
Venue
2015
10.1007/s10822-014-9816-1
Journal of computer-aided molecular design
Keywords
Field
DocType
Multi-targeted ligands,Circular fingerprints,Off-target study,ChE,MAO,Histamine H3 receptor,HMT
Histamine N-methyltransferase,Quantitative structure–activity relationship,chEMBL,Drug discovery,Drug action,In vitro toxicology,Pharmacology,Chemistry,Drug,DrugBank
Journal
Volume
Issue
ISSN
29
2
0920-654X
Citations 
PageRank 
References 
0
0.34
10
Authors
11