Title |
Predicting targets of compounds against neurological diseases using cheminformatic methodology
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Published in |
Perspectives in Drug Discovery and Design, November 2014
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DOI | 10.1007/s10822-014-9816-1 |
Pubmed ID | |
Authors |
Katarina Nikolic, Lazaros Mavridis, Oscar M. Bautista-Aguilera, José Marco-Contelles, Holger Stark, Maria do Carmo Carreiras, Ilaria Rossi, Paola Massarelli, Danica Agbaba, Rona R. Ramsay, John B. O. Mitchell |
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). |
X Demographics
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United Kingdom | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 1% |
United States | 1 | 1% |
Unknown | 71 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Bachelor | 12 | 16% |
Student > Master | 10 | 14% |
Researcher | 9 | 12% |
Student > Ph. D. Student | 8 | 11% |
Student > Doctoral Student | 3 | 4% |
Other | 14 | 19% |
Unknown | 17 | 23% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 13 | 18% |
Chemistry | 12 | 16% |
Pharmacology, Toxicology and Pharmaceutical Science | 6 | 8% |
Psychology | 5 | 7% |
Biochemistry, Genetics and Molecular Biology | 5 | 7% |
Other | 15 | 21% |
Unknown | 17 | 23% |