Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

3rd International Conference on Science, Technology, and Interdisciplinary Research

自然科学;工业技术

Nu'Aim, M.N.^1 ; Bustam, M.A.^1

Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak

32610, Malaysia^1

关键词: Electrophilicity index;    Geometry optimization;    Highest occupied molecular orbital;    Lowest unoccupied molecular orbital;    Multiple linear regressions;    Quantitative structures;    Reactivity descriptor;    Toxicity predictions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/344/1/012017/pdf
DOI  :  10.1088/1757-899X/344/1/012017

来源: IOP

【 摘 要 】

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled "Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids". Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108of ionic liquids can be predicted from this model equation.