TY  - JOUR
AU  - Nikolić, Katarina
AU  - Mavridis, Lazaros
AU  - Bautista-Aguilera, Oscar M.
AU  - Marco-Contelles, Jose
AU  - Stark, Holger
AU  - Carreiras, Maria do Carmo
AU  - Rossi, Ilaria
AU  - Massarelli, Paola
AU  - Agbaba, Danica
AU  - Ramsay, Rona R.
AU  - Mitchell, John B. O.
PY  - 2015
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2395
AB  - 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).
PB  - Springer, Dordrecht
T2  - Journal of Computer-Aided Molecular Design
T1  - Predicting targets of compounds against neurological diseases using cheminformatic methodology
VL  - 29
IS  - 2
SP  - 183
EP  - 198
DO  - 10.1007/s10822-014-9816-1
ER  - 

@article{
author = "Nikolić, Katarina and Mavridis, Lazaros and Bautista-Aguilera, Oscar M. and Marco-Contelles, Jose and Stark, Holger and Carreiras, Maria do Carmo and Rossi, Ilaria and Massarelli, Paola and Agbaba, Danica and Ramsay, Rona R. and Mitchell, John B. O.",
year = "2015",
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).",
publisher = "Springer, Dordrecht",
journal = "Journal of Computer-Aided Molecular Design",
title = "Predicting targets of compounds against neurological diseases using cheminformatic methodology",
volume = "29",
number = "2",
pages = "183-198",
doi = "10.1007/s10822-014-9816-1"
}

Nikolić, 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. in Journal of Computer-Aided Molecular Design
Springer, Dordrecht., 29(2), 183-198.
https://doi.org/10.1007/s10822-014-9816-1

Nikolić K, Mavridis L, Bautista-Aguilera OM, Marco-Contelles J, Stark H, Carreiras MDC, Rossi I, Massarelli P, Agbaba D, Ramsay RR, Mitchell JBO. Predicting targets of compounds against neurological diseases using cheminformatic methodology. in Journal of Computer-Aided Molecular Design. 2015;29(2):183-198.
doi:10.1007/s10822-014-9816-1 .

Nikolić, Katarina, Mavridis, Lazaros, Bautista-Aguilera, Oscar M., Marco-Contelles, Jose, Stark, Holger, Carreiras, Maria do Carmo, Rossi, Ilaria, Massarelli, Paola, Agbaba, Danica, Ramsay, Rona R., Mitchell, John B. O., "Predicting targets of compounds against neurological diseases using cheminformatic methodology" in Journal of Computer-Aided Molecular Design, 29, no. 2 (2015):183-198,
https://doi.org/10.1007/s10822-014-9816-1 . .