The anesthetic action of some polyhalogenated ethers—Monte Carlo method based QSAR study. (August 2018)

• Record Type: Journal Article
• Authors: Golubović, Mlađan; Lazarević, Milan; Zlatanović, Dragan; Krtinić, Dane; Stoičkov, Viktor; Mladenović, Bojan; Milić, Dragan J.; Sokolović, Dušan; Veselinović, Aleksandar M.
• Is Part Of: Computational biology and chemistry. Volume 75(2018)
• Page Start: 32
• Page End: 38
• Publication Date: 2018-08
• Subjects: General anesthetics -- Polyhalogenated ethers -- Quantitative structure-activity relationship -- Monte carlo -- SMILES; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.04.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: QSAR models for polyhalogenated ethers as anesthetics were developed. Molecular fragments responsible for enzyme inhibition are defined. Highly predictive models were obtained using Monte Carlo optimization method. Abstract: Up to this date, there has been an ongoing debate about the mode of action of general anesthetics, which have postulated many biological sites as targets for their action. However, postoperative nausea and vomiting are common problems in which inhalational agents may have a role in their development. When a mode of action is unknown, QSAR modelling is essential in drug development. To investigate the aspects of their anesthetic, QSAR models based on the Monte Carlo method were developed for a set of polyhalogenated ethers. Until now, their anesthetic action has not been completely defined, although some hypotheses have been suggested. Therefore, a QSAR model should be developed on molecular fragments that contribute to anesthetic action. QSAR models were built on the basis of optimal molecular descriptors based on the SMILES notation and local graph invariants, whereas the Monte Carlo optimization method with three random splits into the training and test set was applied for model development. Different methods, including novel Index of ideality correlation, were applied for the determination of the robustness of the model and its predictive potential. The Monte Carlo optimization process was capable of being an efficient in silico tool for building up a robust model of good statistical quality. Molecular fragments which have both positive and negative influence on anesthetic action were determined. The presented study can be useful in the search for novel anesthetics.