1, 3-Oxazole derivatives as potential anticancer agents: Computer modeling and experimental study. (December 2016)
- Record Type:
- Journal Article
- Title:
- 1, 3-Oxazole derivatives as potential anticancer agents: Computer modeling and experimental study. (December 2016)
- Main Title:
- 1, 3-Oxazole derivatives as potential anticancer agents: Computer modeling and experimental study
- Authors:
- Semenyuta, Ivan
Kovalishyn, Vasyl
Tanchuk, Vsevolod
Pilyo, Stepan
Zyabrev, Vladimir
Blagodatnyy, Volodymyr
Trokhimenko, Olena
Brovarets, Volodymyr
Metelytsia, Larysa - Abstract:
- Graphical abstract: Highlights: A series of new predictive QSAR models is presented. Artificial Neural Networks used to build QSAR models. The models demonstrated good predictive ability. Cytotoxic activity of 3 compounds against Hep-2 cell line was the highest. Docking studies of 3 compounds predict their binding to the colchicine binding site of tubulin. Abstract: Microtubules play a significant role in cell growth and functioning. Therefore inhibition of the microtubule assemblies has emerged as one of the most promising cancer treatment strategies. Predictive QSAR models were built on a series of selective inhibitors of the tubulin were performed by using Associative Neural Networks (ANN). To overcome the problem of data overfitting due to the descriptor selection, a 5-fold cross-validation with variable selection in each step of the analysis was used. All developed QSAR models showed excellent statistics on the training (total accuracy: 0.96–0.97) and test sets (total accuracy: 0.95–97). The models were further validated by 11 synthesized 1, 3-oxazole derivatives and all of them showed inhibitory effect on the Hep-2 cancer cell line. The most promising compound showed inhibitory activity IC50 = 60.2 μM. In order to hypothesize their mechanism of action the top three compounds were docked in the colchicine binding site of tubulin and showed reasonable docking scores as well as favorable interactions with the protein.
- Is Part Of:
- Computational biology and chemistry. Volume 65(2016)
- Journal:
- Computational biology and chemistry
- Issue:
- Volume 65(2016)
- Issue Display:
- Volume 65, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 65
- Issue:
- 2016
- Issue Sort Value:
- 2016-0065-2016-0000
- Page Start:
- 8
- Page End:
- 15
- Publication Date:
- 2016-12
- Subjects:
- Tubulin inhibitors -- Drug design -- Qsar -- Molecular docking -- Associative Neural Networks (ASNN)
Chemistry -- Data processing -- Periodicals
Biology -- Data processing -- Periodicals
Biochemistry -- Data processing
Biology -- Data processing
Molecular biology -- Data processing
Periodicals
Electronic journals
542.85 - Journal URLs:
- http://www.sciencedirect.com/science/journal/14769271 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compbiolchem.2016.09.012 ↗
- Languages:
- English
- ISSNs:
- 1476-9271
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3390.576700
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 7632.xml