Research Journal of Chemical Sciences ______________________________________________ ISSN 2231-606X Vol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 29 Quantum Chemical Descriptors Based QSTR Study of Nitrobenzene Derivatives against Tetrahymena PyriformisMishra Sunil Kumar*, Mishra Vibhanjali, Tripathi P.N. and Khan Mohd. Adil1 Department of Chemistry, M.L.K. PG College, Balrampur, UP, INDIA Department of Chemistry, Kisan PG College, Bahraich, UP, INDIAAvailable online at: www.isca.in, www.isca.me Received 26th December 2013, revised 6th January 2014, accepted 4th February 2014Abstract Eight quantum chemical descriptors namely molecular weight, molar refractivity, HOMO energy, electronegativity, electron affinity, ionization potential, total energy and Log P of fifty four nitrobenzene derivatives have been calculated with the help of CAChe Pro of Fujitsu software using DFT methods and the semiemperical PM3 methods. Observed toxicities of all compounds are in terms of -log (IGC50), mM, which is the inverse logarithm of the concentration causing 50% growth inhibition of Tetrahymena pyriformis after 40 hours. These eight descriptors have been used in the development of QSTR models. The QSTR model developed from descriptors molecular weight, molar refractivity, electron affinity and total energy have very high predictive power and can be used to find out the toxicity of any new derivative of nitrobenzene. Reliable QSTR models have been obtained from single descriptors namely electron affinity and total energy. The quality of regression has been adjudged by correlation coefficient, cross validation coefficient and statistical parameters like standard error, standard error of estimate, p-value, t-value, degrees of freedom etc. Keywords: Nitrobenzene derivatives, tetrahymena pyriformis, DFT, electron affinity, total energy. Introduction The toxicity of nitrobenzenes against Tetrahymena pyriformishas been extensively studied by using 2D and 3D QSAR methodologies1-4. Hydrophobicity and electrophilic reactivity appeared the most important structural factors contributing to the toxic action of nitrobenzene. Nitrobenzene and their numerous derivatives are of use as explosives and propellants in the military and in industry6,7. Waste from nitro compounds are easily disseminated leading to a potential hazard for humans and the environment. A number of studies have shown that nitro compounds, as well as their metabolites of environmental transformation, by-products of synthesis, or incomplete combustion are harmful for the biosphere due to their toxicity9-11. For instance, toxic effects in humans include gastrointestinal, neurological and reproductive disorders, cirrhosis of the liver, hepatitis, cataracts, respiratory and skin irritation, nephrotoxicity, and hematological defects. Moreover, nitrobenzene derivatives are widely used in medicine, industry and agriculture. Nitroaromatic pesticides as well as the explosive residues are considered as toxic environmental pollutants. Some of these compounds have mutagenic or carcinogenic activity and may accumulate in the food chain (bioaccumulation). Therefore, the presence of aromatic and nitroaromatic xenobiotics in the environment may present serious public health and environmental problems. Both nature and degree of aromatic substitutions may have effects on the chemical toxicity of nitroaromatic compounds12. In recent years various descriptors like quantum chemical, topological and energy descriptors have been successfully employed for QSTR and QSAR studies of different compunds13-19. In this paper, Quantum chemical descriptors have been used for the development of QSTR models for Fifty four nitrobenzene derivatives. The descriptors that have been used are molecular weight, molar refractivity, HOMO energy, electronegativity, electron affinity, ionization potential, total energy and Log P. The predicted toxicities obtained from developed QSTR models were found close to reported observed toxicities. Material and Methods Fifty four substituted nitrobenzene derivatives given in table-1 have been taken as study material. The toxicity of these compounds was measured in terms of -log (IGC50), mM, which is the inverse logarithm of the concentration causing 50% growth inhibition of Tetrahymena pyriformis after 40 hours. The 3D modeling and geometry optimization of all the compounds and evaluation of values of descriptors have been done with the help of CAChe Pro software of Fujitsu, using the DFT Methods20-22 and semiemperical PM3 Hamiltonian23. The Project Leader program has been used for multi linear regression (MLR) analysis. The statistical parameters have been calculated by Smith’s Statistical Package (version 2.80). The descriptors that have been used are described below. Water/Octanol Partition coefficient (Log P)24: The Water/Octanol partition coefficient is the ratio of concentrations of un-ionized compound between the two solutions. To measure the partition coefficient of ionizable solutes, the pH of the aqueous phase is adjusted such that the predominant form of the Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 30 compound is un-ionized. The logarithm of the ratio of the concentrations of the un-ionized solute in the solvents is called log P Molar Refractivity25: It is a constitutive-additive property that is calculated by the Lorenz-Lorentz formula, MR = -1 +1 * M where M is the molecular weight, n is the refraction index and is the density. For a radiation of infinite wavelength, the molar refractivity represents the real volume of the molecules. Molar refractivity is related, not only to the volume of the molecules but also to the London dispersive forces that act in the drug-receptor interaction. Molecular Weight: It is the sum of atomic weights of all the atoms of the compound. Total energy: Total energy (TE) of a molecular system is sum of the total electronic energy (Eee) and the energy of internuclear repulsion (Enr)26. TE = Eee + Enr The total electronic energy of the system is given by Eee =1 /2 P (H +F) Where P is the density matrix, H is the one-electron matrix, and F is the Fock matrix. HOMO Energy: The energy required to remove an electron from the highest occupied molecular orbital (HOMO) is called HOMO energy. Electronegativity,Ionisation Potential and Electron Affinity27,28: Parr et al define the electronegativity as the negative of chemical potential, = – = – (E/N) (r) (1) The operational definition of absolute hardness, global softness and electronegativity is as = – 1 / 2 (IP+EA) (2)where IP and EA are the ionization potential and electron affinity respectively, of the chemical species. According to the Koopman’s theorem, the IP is simply the eigen value of HOMO with change of sign and EA is the eigen value of LUMO with change of sign, hence we have 1/2 ( LUMO + HOMO) (3) Table-1 Nitrobenzene Derivatives used in our study along with their Observed toxicity S. No. Compounds Observed Toxicity log(IGC 50 ) 1 Nitrobenzene 0.14 2 2-Chloronitrobenzene 0.68 3 2-Bromonitrobenzene 0.75 4 3-Chloronitrobenzene 0.73 5 4-Ethylnitrobenzene 0.80 6 4-Chloronitrobenzene 0.43 7 4-Bromonitrobenzene 0.38 8 4-Fluoronitrobenzene 0.25 9 2,4,6-Trimethylnitrobenzene 0.86 10 2,4-Dichloronitrobenzene 0.99 11 3-Bromonitrobenzene 1.03 12 2,3-Dichloronitrobenzene 1.07 13 3-Methyl-4-bromonitrobenzene 1.16 14 3,4-Dichloronitrobenzene 1.16 15 1,2-Dinitrobenzene 1.25 16 1,4-Dinitrobenzene 1.30 17 2,5-Dibromonitrobenzene 1.37 18 4-Butoxynitrobenzene 1.42 19 2,4,6-Trichloronitrobenzene 1.43 20 2,3,4-Trichloronitrobenzene 1.51 21 5-methyl-1,2-dinitrobenzene 1.52 22 2,4,5-Trichloronitrobenzene 1.53 23 2,3,4,5-Tetrachloronitrobenzene 1.78 24 2,3,5,6-Tetrachloronitrobenzene 1.82 25 6-Iodo-1,3-dinitrobenzene 2.12 26 2,4,6-Trichloro-1,3-dinitrobenzene 2.19 27 1,2-Dinitro-4,5-dichlorobenzene 2.21 28 6-Bromo-1,3-dinitrobenzene 2.31 29 2,4,5-Trichloro-1,3-dinitrobenzene 2.59 30 4,6-Dichloro-1,2-dinitrobenzene 2.42 31 2,3,5,6-Tetrachloro-1,4-dinitrobenzene 2.74 32 1,3-Dimethyl-2-nitrobenzene 0.30 33 2,3-Dimethylnitrobenzene 0.56 34 3,5-Dichloronitrobenzene 1.13 35 3-Chloro-4-fluoronitrobenzene 0.80 36 2.5-Dichloronitrobenzene 1.13 37 1,2,3-Trifluoro-4-nitrobenzene 1.89 38 2,3,4,6-Tetrafluoronitrobenzene 1.87 39 1-Chloro-2,4-dinitrobenzene 2.16 40 2,4-Dinitro-1-fluorobenzene 1.71 41 Pentafluoronitrobenzene 2.43 42 1,5-Difluoro-2,4-dinitrobenzene 2.08 43 1,2-Dimethyl-4-nitrobenzene 0.59 44 1-Fluoro-3-iodo-5-nitrobenzene 1.09 45 1-Fluoro-2-nitrobenzene 0.23 46 1,2,3-Trichloro-5-nitrobenzene 1.55 47 1,3-Dichloro-4,6-dinitrobenzene 2.72 48 2,6-Dimethylnitrobenzene 0.30 49 2-Methyl-3-chloronitrobenzene 0.68 50 2-Methylnitrobenzene 0.05 51 2-Methyl-5-chloronitrobenzene 0.82 52 6-Chloro-1,3-dinitrobenzene 1.98 53 3-Methylnitrobenzene 0.05 54 4-Methylnitrobenzene 0.17 Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 31 Results and Discussion Fifty four derivatives of nitrobenzeneare given in table-1 along with their observed toxicity in terms of -log (IGC50). The values of eight descriptors of compounds, which have been calculated, are given in table-2. For the development of QSTR models multi linear regression (MLR) analysis has been performed using different combinations of descriptors. The MLR analysis has indicated that the toxicity of nitrobenzenecan be successfully modeled even in mono-parametric regression using descriptors electron affinity and total energy. The mono-parametric QSTR model obtained by using descriptor total energy is given by following regression equation, Mono-PT1 = -0.0346854*E - 2.22073 2 = 0.842424, rCV2 = 0.817187, Std. Error = 0.0600, SEE = 0.3029, t-value = 16.6692, p-value = 0, DOF = 0.8393, N = 54, VC = 1. and the mono-parametric QSTR model obtained by using descriptor electron affinity is given by following regression equation, Mono-PT2 = 1.44437*E - 1.04585 2 = 0.736855, rCV2 = 0.708911, Std. Error = 0.0829, SEE = 0.3913, t-value = 12.0659, p-value = 0, DOF = 0.7318, N = 54, VC = 1. In the above regression equations, r2 is correlation coefficient, rCV2 is cross-validation coefficient, Std. Error is standard error, SEE is standard error of estimate, DOF is degrees of freedom, N is data points (compounds), and VC is variable count. Total energy and electron affinity appear important descriptor for this set of nitrobenzenederivatives. The trends of observed toxicity and predicted toxicity obtained from Mono-PT1 and Mono-PT2 are shown in figure-1 and figure-2. The predicted toxicities, obtained from above two mono-parametric QSTR models Mono-PT1 and Mono-PT2, are listed in table-3. The addition of other descriptor in the above mono-parametric model yields a model with improved predictability. The resulting bi-parametric QSTR model obtained by using descriptors molecular weight and total energy is given by following regression equation, Bi-PT10.00498977*MW0.0271056*E2.44255 2 = 0.891916, rCV2 = 0.869526 Std. Error = 0.0483, SEE = 0.2508, t-value = 20.7132, p-value = 0, DOF = 0.8898, N = 54, VC = 2. and the bi-parametric QSTR model developed from descriptors molar refractivity and electron affinity is given by following regression equation, Bi-PT2 = 0.0480157*MR + 1.26113*E - 2.86509 2 = 0.87697, rCV2 = 0.849372, Std. Error = 0.0519, SEE = 0.2675, t-value = 19.2554, p-value = 0, DOF = 0.8746, N = 54, VC = 2. The trends of observed toxicity and predicted toxicity obtained from Bi-PT1 and Bi-PT2 are shown in figure-3 and figure-4. The predicted toxicities, obtained from above two bi-parametric QSTR models Bi-PT1 and Bi-PT2, are listed in table-3. Using combination of three descriptors, the tri-parametric QSTR models are obtained with improved predictive power. The best two are discussed here, Tri-PT1 = 0.0346058*MR + 0.675744*E - 0.0162879*E - 2.97539 2 = 0.908686, rCV2 = 0.866198, Std. Error = 0.0439, SEE = 0.2304, t-value = 22.7567, p-value = 0, DOF = 0.9070, N = 54, VC = 3. This QSTR model involves molar refractivity as first descriptor, electron affinity as second descriptor and total energy as third descriptor. Tri-PT2 = 0.00491374*MW +0.361497*E - 0.0200948*E - 2.3013 2 = 0.902500, rCV2 = 0.877689, Std. Error = 0.0456, SEE = 0.2382, t-value = 21.9356, p-value = 0, DOF = 0.9006, N = 54, VC = 3. This QSTR model involves molecular weight as first descriptor, electron affinity as second descriptor and total energy as third descriptor. The trends of observed toxicity and predicted toxicity obtained from Tri-PT1 and Tri-PT2 are shown in figure-5and figure-6. The predicted toxicities, obtained from above two tri-parametric QSTR models Tri-PT1 and Tri-PT2, are listed in table-3. By the combination of four descriptors, tetra-parametric QSTR models are obtained with excellent predictive power. The best two are discussed here, Tetra-PT1 = 0.00228773*MW + 0.0232587*MR + 0.569215*E- 0.0165772* E - 2.78577 2 = 0.913259, rCV2 = 0.875215, Std. Error = 0.0427, SEE = 0.2247, t-value = 23.3928, p-value = 0, DOF = 0.9116, N = 54, VC = 4. This QSTR model is obtained by using the descriptors molecular weight, molar refractivity, electron affinity and total energy. The values of correlation coefficient and cross validation coefficient indicate that this model has excellent predictive power and can be used to find out the toxicity of any nitrobenzene derivative. Tetra-PT2 = 0.00385828*MW + 0.0230104*MR - 0.389962* -0.0203582*E - 4.88179 2 = 0.91214, rCV2 = 0.886503, Std. Error = 0.0430, SEE = 0.2261, t-value = 23.2320, p-value = 0, DOF = 0.9104, N = 54, VC = 4. This QSTR model is obtained by using the descriptors molecular weight, molar refractivity, electronegativity and total energy. The values of correlation coefficient and cross validation coefficient indicate that this model has excellent predictive power and can be used to find out the toxicity of any nitrobenzene derivative. The trends of observed toxicity and predicted toxicity obtained from Tetra-PT1 and Tetra-PT2 are shown in figure-7 and figure-8. The predicted toxicities, obtained from above two tetra-parametric QSTR models Tetra-PT1 and Tetra-PT2, are listed in table-3. Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 32 Table-2 Values of descriptors and observed toxicity of Nitrobenzene Derivatives C. No. MW MR E HOMO EA IP E T LogP log(IGC 50 ) 1 123.111 33.383 -10.603 -1.134 1.134 10.603 -69.403 2.000 0.14 2 157.556 38.188 -9.944 -1.267 1.267 9.944 -81.165 2.518 0.68 3 202.007 41.006 -10.396 -1.286 1.286 10.396 -79.285 2.792 0.75 4 157.556 38.188 -10.063 -1.306 1.306 10.063 -81.168 2.518 0.73 5 151.165 43.025 -10.410 -1.087 1.087 10.410 -83.750 2.864 0.80 6 157.556 38.188 -10.219 -1.356 1.356 10.219 -81.172 2.518 0.43 7 202.007 41.006 -10.702 -1.389 1.389 10.702 -79.290 2.792 0.38 8 141.101 33.599 -10.845 -1.415 1.415 10.845 -85.318 2.140 0.25 9 165.191 48.506 -9.946 -1.020 1.020 9.946 -90.951 3.402 0.86 10 192.001 42.992 -10.049 -1.471 1.471 10.049 -92.934 3.036 0.99 11 202.007 41.006 -10.524 -1.354 1.354 10.524 -79.290 2.792 1.03 12 192.001 42.992 -9.788 -1.396 1.396 9.788 -92.930 3.036 1.07 13 216.034 46.047 -10.418 -1.323 1.323 10.418 -86.474 3.259 1.16 14 192.001 42.992 -9.971 -1.486 1.486 9.971 -92.938 3.036 1.16 15 168.109 40.707 -11.323 -1.967 1.967 11.323 -101.181 1.954 1.25 16 168.109 40.707 -11.305 -2.253 2.253 11.305 -101.204 1.954 1.30 17 280.903 48.628 -10.395 -1.493 1.493 10.395 -89.171 3.584 1.37 18 195.218 53.719 -10.013 -1.006 1.006 10.013 -110.252 2.955 1.42 19 226.446 47.797 -9.888 -1.567 1.567 9.888 -104.692 3.554 1.43 20 226.446 47.797 -9.852 -1.564 1.564 9.852 -104.700 3.554 1.51 21 182.135 45.749 -11.040 -1.923 1.923 11.040 -108.372 2.421 1.52 22 226.446 47.797 -9.768 -1.596 1.596 9.768 -104.700 3.554 1.53 23 260.891 52.602 -9.741 -1.683 1.683 9.741 -116.464 4.072 1.78 24 260.891 52.602 -9.523 -1.614 1.614 9.523 -116.451 4.072 1.82 25 294.005 53.116 -9.723 -1.980 1.980 9.723 -110.008 3.211 2.12 26 271.444 55.122 -10.298 -2.171 2.171 10.298 -136.479 3.508 2.19 27 236.999 50.317 -10.440 -2.241 2.241 10.440 -124.716 2.990 2.21 28 247.005 48.330 -11.150 -2.100 2.100 11.150 -111.085 2.746 2.31 29 271.444 55.122 -10.072 -2.173 2.173 10.072 -136.481 3.508 2.59 30 236.999 50.317 -10.467 -2.166 2.166 10.467 -124.700 2.990 2.42 31 305.889 59.927 -9.798 -2.489 2.489 9.798 -148.227 4.026 2.74 32 151.165 43.465 -9.981 -1.044 1.044 9.981 -83.760 2.935 0.30 33 151.165 43.465 -10.036 -1.047 1.047 10.036 -83.769 2.935 0.56 34 151.165 43.465 -10.091 -1.036 1.036 10.091 -83.780 2.935 1.13 35 175.547 38.404 -10.176 -1.551 1.551 10.176 -97.084 2.658 0.80 36 192.001 42.992 -9.740 -1.429 1.429 9.740 -92.929 3.036 1.13 37 177.082 34.032 -11.025 -1.932 1.932 11.025 -117.150 2.419 1.89 38 195.073 34.248 -10.864 -2.181 2.181 10.863 -133.057 2.558 1.87 39 202.554 45.512 -10.656 -2.087 2.087 10.656 -112.970 2.472 2.16 40 186.099 40.924 -11.544 -2.194 2.194 11.544 -117.123 2.093 1.71 41 213.063 34.465 -11.157 -2.435 2.435 11.157 -148.983 2.698 2.43 42 204.090 41.140 -11.822 -2.421 2.421 11.822 -133.041 2.233 2.08 43 151.165 43.465 -10.165 -1.055 1.055 10.165 -83.777 2.935 0.59 44 266.998 46.007 -9.662 -1.534 1.534 9.662 -94.124 3.397 1.09 45 141.101 33.599 -10.629 -1.386 1.386 10.629 -85.318 2.140 0.23 46 226.446 47.797 -10.038 -1.608 1.608 10.038 -104.703 3.554 1.55 47 236.999 50.317 -10.694 -2.191 2.191 10.694 -124.735 2.990 2.72 48 151.165 43.465 -9.980 -1.043 1.043 9.980 -83.760 2.935 0.30 49 171.583 43.229 -9.923 -1.258 1.258 9.923 -88.350 2.985 0.68 50 137.138 38.424 -10.238 -1.091 1.091 10.238 -76.587 2.467 0.05 51 171.583 43.229 -9.839 -1.259 1.259 9.840 -88.351 2.985 0.82 52 202.554 45.512 -10.656 -2.086 2.086 10.656 -112.970 2.472 1.98 53 137.138 38.424 -10.271 -1.082 1.082 10.271 -76.591 2.467 0.05 54 137.138 38.424 -10.472 -1.109 1.109 10.472 -76.594 2.467 0.17 where MW = molecular weight, MR = Molar Refractivity, EHOMO = Energy of HOMO, = Electronegativity, EA = Electron Affinity, IP = Ionization Potential, E = Toatl Energy Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 33 Table-3 Predicted Toxicities MonoPT1 to TetraPT2 of the Nitrobenzene Derivatives C. No. Mono PT1 Mono PT2 Bi PT1 Bi PT2 Tri PT1 Tri PT2 Tetra PT1 Tetra PT2 1 0.187 0.593 0.053 0.168 0.077 0.108 0.069 0.063 2 0.594 0.784 0.544 0.566 0.524 0.562 0.53 0.443 3 0.529 0.812 0.714 0.726 0.604 0.749 0.676 0.733 4 0.595 0.84 0.544 0.615 0.55 0.576 0.552 0.474 5 0.684 0.524 0.582 0.572 0.612 0.517 0.568 0.638 6 0.595 0.913 0.544 0.679 0.585 0.594 0.58 0.514 7 0.529 0.96 0.715 0.855 0.674 0.787 0.735 0.813 8 0.739 0.997 0.574 0.532 0.533 0.618 0.538 0.563 9 0.934 0.428 0.847 0.751 0.874 0.707 0.809 0.862 10 1.003 1.078 1.035 1.054 1.02 1.041 1.031 0.986 11 0.529 0.91 0.715 0.812 0.65 0.774 0.715 0.771 12 1.003 0.971 1.034 0.96 0.97 1.014 0.989 0.921 13 0.779 0.865 0.979 1.014 0.921 0.976 0.966 1.061 14 1.003 1.101 1.035 1.074 1.03 1.047 1.04 0.974 15 1.289 1.795 1.139 1.57 1.411 1.269 1.343 1.355 16 1.29 2.209 1.139 1.931 1.604 1.373 1.506 1.407 17 0.872 1.111 1.376 1.353 1.169 1.411 1.316 1.454 18 1.603 0.407 1.52 0.983 1.359 1.237 1.31 1.501 19 1.411 1.217 1.525 1.406 1.442 1.481 1.471 1.457 20 1.411 1.214 1.525 1.403 1.441 1.481 1.47 1.449 21 1.538 1.732 1.404 1.757 1.672 1.467 1.586 1.607 22 1.411 1.26 1.525 1.443 1.463 1.492 1.488 1.439 23 1.819 1.385 2.016 1.783 1.879 1.929 1.923 1.934 24 1.818 1.286 2.016 1.697 1.833 1.904 1.884 1.878 25 1.595 1.814 2.006 2.182 1.992 2.07 2.073 1.996 26 2.513 2.09 2.611 2.52 2.622 2.56 2.616 2.644 27 2.105 2.191 2.121 2.377 2.311 2.179 2.27 2.202 28 1.632 1.988 1.801 2.104 1.926 1.904 1.94 2.028 29 2.513 2.093 2.611 2.523 2.624 2.561 2.617 2.6 30 2.105 2.083 2.12 2.282 2.261 2.152 2.227 2.192 31 2.921 2.549 3.102 3.151 3.194 3.08 3.182 3.091 32 0.685 0.462 0.582 0.538 0.598 0.502 0.554 0.556 33 0.685 0.467 0.582 0.543 0.601 0.503 0.556 0.568 34 0.685 0.451 0.583 0.529 0.594 0.5 0.55 0.577 35 1.147 1.195 1.065 0.935 0.983 1.073 1.001 0.942 36 1.003 1.019 1.034 1.002 0.992 1.026 1.008 0.918 37 1.843 1.744 1.616 1.205 1.416 1.621 1.452 1.496 38 2.394 2.105 2.137 1.53 1.851 2.119 1.904 1.911 39 1.698 1.968 1.63 1.952 1.85 1.718 1.797 1.732 40 1.842 2.123 1.661 1.867 1.831 1.76 1.782 1.841 41 2.947 2.471 2.659 1.861 2.289 2.62 2.359 2.417 42 2.394 2.452 2.182 2.164 2.251 2.25 2.222 2.338 43 0.685 0.478 0.583 0.553 0.606 0.506 0.56 0.595 44 1.044 1.17 1.441 1.279 1.186 1.457 1.329 1.306 45 0.739 0.956 0.574 0.496 0.513 0.607 0.522 0.515 46 1.411 1.277 1.525 1.458 1.471 1.497 1.495 1.494 47 2.106 2.119 2.121 2.314 2.278 2.162 2.242 2.242 48 0.685 0.461 0.582 0.538 0.598 0.502 0.553 0.556 49 0.844 0.771 0.808 0.797 0.809 0.772 0.793 0.754 50 0.436 0.531 0.318 0.356 0.339 0.306 0.313 0.3 51 0.844 0.773 0.808 0.798 0.81 0.772 0.794 0.738 52 1.698 1.968 1.63 1.951 1.849 1.718 1.796 1.731 53 0.436 0.517 0.318 0.344 0.333 0.303 0.307 0.304 54 0.436 0.556 0.318 0.378 0.351 0.313 0.323 0.349 Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 34 Figure-1 Trend of observed toxicity and predicted toxicity (obtained from MonoPT1) of the Nitrobenzene derivatives Figure-2 Trend of observed toxicity and predicted toxicity (obtained from MonoPT2) of the Nitrobenzene derivatives Figure-3 Trend of observed toxicity and predicted toxicity (obtained from BiPT1) of the Nitrobenzene derivatives Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 35 Figure-4 Trend of observed toxicity and predicted toxicity (obtained from BiPT2) of the Nitrobenzene derivatives Figure-5 Trend of observed toxicity and predicted toxicity (obtained from TriPT1) of the Nitrobenzene derivatives Figure-6 Trend of observed toxicity and predicted toxicity (obtained from TriPT2) of the Nitrobenzene derivatives Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 36 Figure-7 Trend of observed toxicity and predicted toxicity (obtained from TetraPT1) of the Nitrobenzene derivatives Figure-8: Trend of observed toxicity and predicted toxicity (obtained from TetraPT2) of the Nitrobenzene derivatives ConclusionIt is clear from the above study that, the best combination of Quantum chemical descriptors is molecular weight, molar refractivity, electron affinity and total energy for the QSTR study of nitrobenzene derivatives against Tetrahymena pyriformis. Reliable QSTR models have been obtained from single descriptors namely electron affinity and total energy. Therefore, electron affinity and total energy appear good descriptors for QSTR study of nitrobenzene derivatives. References1.Cronin M.T.D. and Schultz T.W., Development of Quantitative Structure-Activity Relationships for the Toxicity of Aromatic Compounds to Tetrahymena pyriformis: Comparative Assessment of the Methodologies, Chem. Res. Toxicol., 14, 1284-1295, (2001)2.Katritzky A.R., Oliferenko P., Oliferenko A., Lomaka A. and Karelson M., Nitrobenzene toxicity: QSAR correlations and mechanistic interpretations, J. Phys. Org. Chem., 16, 811-817, (2003) 3.Kuzmin V.E., Muratov E.N., Artemenko A.G., Gorb L.G., Qasim, M. and Leszczynski J., The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study, J. Comp. Aid. Mol. Des., 22, 747-759, (2008)4.Agrawal W.K. and Khadikar P.V., QSAR prediction of toxicity of nitrobenzenes, Bioorg. Med. Chem., 3035-3040, (2001)5.Cronin M.T.D., Gregory B.W. and Schultz T.W., Quantitative Structure-Activity Analyses of Nitrobenzene Toxicity to Tetrahymena pyriformis, Chem. Res.Toxicol.,11, 902-908, (1998) Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606XVol. 4(2), 29-37, February (2014) Res. J. Chem. Sci. International Science Congress Association 37 6.Patai, S., The Chemistry of Amino, Nitroso, and Nitro Compounds and Their Derivatives. New York, USA: John Wiley & Sons Inc., (1982)7.Feuer H. and Nielsen A.T., Nitro Compounds: Recent Advances in Synthesis and Chemistry, VCH Publishing, New York, (1990)8.Neilson A.H. and Allard A.S., Environmental Degradation and Transformation of Organic Chemicals. Boca Raton, Florida: CRC Press, (2008)9.Talmage S.S., Opresko D.M., Maxwell C.J., Welsh C.J., Cretella F.M., Reno, P.H. and Daniel F.B., Nitroaromatic munition compounds: environmental effects and screening values, Rev. Environ. Contam. Toxicol., 161, 1-156, (1999)10.Rickert D.E., Toxicity of Nitroaromatic Compounds. Bristol, Pennsylvania, Hemisphere Publishing Corp, (1984)11.Robidoux P.Y., Svendsen C., Caumartin J., Hawari J., Ampleman G., Thiboutot S., Weeks J.M. and Sunahara G.I., Chronic toxicity of energetic compounds in soil using the earthworm (Eisenia andrei) reproduction test, Environ. Toxicol. Chem.19, 1764-1773, (2000)12.Donlon B.A., Razo-Flores E., Field J.A. and Lettinga G., Toxicity of N-substituted aromatics to acetoclastic methanogenic activity in granular sludge, Appl. Environ. Microbiol.,61, 3889-3893, (1995)13.Hall L.H. and Vaughn T.A., QSAR of Phenol Toxicity using Electrotopological State and Kappa Shape Indices, Med. Chem. Res.,, 407-416, (1997)14.Pasha F.A., Srivastava H.K. and Singh P.P., QSAR Study of Estrogens with the help of PM3 Based Descriptors, Int. J. Quantum Chem., 104 (1), 87-100, (2005)15.Pasha F.A., Srivastava H.K. and Singh P.P., Comparative QSAR Study of Phenol Derivatives with the help of Density Functional Theory, Bioorg. Med. Chem., 13(24), 6823-6829, (2005)16.Singh Rajeev, Kumar D., Singh Bhoop, Singh V.K. and Sharma Ranjana, Molecular structure, vibrational spectroscopic and HOMO, LUMO studies of S-2-picolyl- N-(2- acetylpyrrole) dithiocarbazate Schiff base by Quantum Chemical investigations, Research Journal of Chemical Sciences, 3(2), 79-84, (2013)17.Gupta Y.K., Agarwal S.C., Madnawat S.P. and Ram Narain, Synthesis, Characterization and Antimicrobial Studies of Some Transition Metal Complexes of Schiff Bases, Research Journal of Chemical Sciences, 2(4), 68-71, (2012)18.Buttrus H. Nabeel and Saeed T. Farah, Synthesis and Structural Studies on Some Transition metal complexes of Bis-(benzimidazole-2-thio) ethane, propane and butane ligands, Research Journal of Chemical Sciences, 2(6), 43-49, (2012)19.Singh B.N., Singh K. and Ahmad K., QSAR Study of Rabbit Aortic Angiotensin II Antagonists Compounds Using Different Descriptors, Research Journal of Chemical Sciences, 3(4), 81-83, (2013)20.Parr R.G. and Yang W., Density-Functional Theory of Atoms and Molecules, Oxford University Press: New York, (1989)21.Perdew J.P. and Kurth S., A Primer in Density Functional Theory, Springer: Berlin, (2003)22.Koch W. and Holthausen M.C., A Chemist’s Guide to Density Functional Theory,Wiley-VCH: New York, (2000)23.Bingham R.C., Dewar M.J.S. and Lo D.H., Ground states of molecules. XXV. MINDO/3. Improved version of the MINDO semiempirical SCF-MO method, J. Am. Chem. Soc.97(6), 1285-1293, (1975)24.Pulay P., Ruoff A. and Sawodny W., Ab initio Hartree-Fock Calculation of the Force Constants of the Linear Molecules HCN, FCN, (CN) and the Ion N, Mol. Phys., 30, 1123-1130, (1975)25.Padron R.J., Carrasco, A. and Pellon R.F., Molecular descriptor based on a molar refractivity partition using Randic-type Figure-theoretical invariant, J. Pharm. Pharmaceut. Sci., 5(3), 258-266, (2002) 26.Parr R.G. and Weitao Y., Density-functional theory of the electronic structure of molecules, Ann. Rev. Phys. Chem., 46, 701-728, (1995)27.Sanderson R.T., Chem Bond and Bond Energy, Academic Press: New York, 19, (1971)28.Parr R.G. and Pearson R.G., J. Am. Chem. Soc., 105, 7512-7516, (1983)