Zhang et al. (2009) combined DFT-based conformation analysis with QSAR to develop a simple new DFT/QSAR approach to predict the nature of bioactive conformation in the side chain of cyclic imide derivatives as protoporphyrinogen oxidase inhibitors. The potential energy surface scan, molecular docking and molecular dynamic simulation supported the reliability of this new approach. Damme and Bultinck (2010) employed conceptual DFT bsed quantum chemical molecular field in order to construct 3D-QSAR models to describe the antituberculotic activity of salicylamide derivatives. QSAR studies on the interactions between the terpenoid mosquito repellents and lactic acid were made by Song et al. (2013). They found that in addition to the structures of the repellents, the repellent–lactic acid complexes also have a role in deciding their activities. Use of ordered predictors selection algorithm and partial least squares regression was also made to select variables and to construct QSAR models of various 4,5-dihydroxypyrimidine carboxamide derivatives as HIV-1 integrase inhibitors (de Melo et al., 2009). The electronic distribution of the studied compounds appeared to have deciding role in their inhibition activities. The antifungal activity of N-nitrosopiperidone semicarbazone derivatives against the plant pathogens viz., Fusarium oxysporum and Rhizoctonia solani can also be predicted by the regression models devepoled by the descriptors like molecular electrostatic potential, charge on N center, dipole moment, minium surface area and log P (Hemalatha et al., 2012). The QSAR studies on the imidazole and benz­imidazole derivatives as corrosion inhibitors revealed that HOMO energy, polarizability, dipole, frontier orbital charge density, the interaction mode between inhibitors and metal surface and steric hindrance of molecules are deciding descriptors to represent their inhibiting potency (Zhang et al., 2005). Various DFT based descriptors like HOMO-LUMO energy gap, HOMO and LUMO energies, mean molecular polarizability, atomic charge, weighted nucleophilic atomic frontier electron density were employed in order to build regression models for the various cyclic imide derivatives as protoporphyrinogen oxidase (Protox) inhibitors (Wan et al., 2004). The DFT-based QSAR and 3D-QSAR approaches on the activity of aurone derivatives against chloroquine resistant parasite by Adhikari et al. (2013) are also notworthy. A review summarizing various quantum-chemical descriptors in developing QSAR and QSPR written by Karelson and Lobanov (1996) is highly recommended to get more details about these approaches.

Geometrical descriptors were designed and included in the QSAR during the development of 3D­QSAR models in mid 1980s. These included gravitational indices (Katritzky et al., 1996), shadow indices (Rohrbaugh & Jurs, 1987), EVA descriptors (Ferguson et al., 1997), GETAWAY descriptors (Consonni et al., 2002), 3D-Morse descriptors (Schuur et al., 1996), charged partial surface area descriptors (Stanton & Jurs, 1990), and WHIM descriptors (Todeschini et al., 1994).

CoMFA (Cramer et al., 1988) is an effective method in building effective 3D-QSAR, which was successfully applied in drug designing and other related applications (Aboye et al., 2004; Akamatsu, 2002; Amin & Welsh, 2006; Avery et al., 2002; Bhongade & Gadad, 2004; Hannongbua etal., 2001; Jayatilleke et al., 2000; Sippl & Holtje, 2000). History behind the idea of CoMFA in modeling QSAR is that in the classical QSAR, the different biological acitivies of drugs or other compounds of interest are correlated with various physico-chemical properties or quantum-chemical descriptors, provided

Further, quantitative structure-toxicity relationship (QSTR) is essential in order to have proper knowl­edge about the toxicity of various essential systems; hence it is an issue of environmental safety and security. The approach of QSTR modeling even becomes more popular since experimental determina­tion of toxicity of each and every essential compound is hard with respect to time, cost, and the avail­ability of resources. Different theoretical approaches have been made to connect the structural features (electronic, geometric, topological, quantum chemical, etc.) with the toxicity. An effective QSTR model could predict the relative toxicity/safety of different systems in a reliable way saving the experimental effort. Cronin and Schultz (1998) developed useful QSTR model for vibrio pscheri toxicity including all probable mechanisms. Schultz et al. (1999, 2002) evaluated toxicity of numerous aliphatic chemicals and substituted benzenes towards Tetrahymena pyriformis.

The databases regarding toxicity of a number of fish species like the guppy (Poecilia reticulata) and zebrafish (Brachydanio rerio) are also available. The fish data sets for the fathead minnow (Pimephales promelas) to predict the acute toxicity and related modes of toxic action was built by studying the es­tablishment of toxicodynamic profiles, and behavioral and dose-response interpretation of 96-h LC

Cronin et al. (1998) developed QSTR model by using LUMO energy and maximum acceptor super­delocalizability as descriptors to predict toxicity of various alkyl- and halogen-substituted nitro- and dinitrobenzenes towards T. Pyriformis. Mishra et al. (2014) employed various quantum-chemical descrip- tors like HOMO energy, electronegativity, electron affinity (EA), ionization potential (IP), total energy and log P to predict the toxicity of nitrobenzene derivatives towards T. Pyriformis. The topological descriptors were also found to be effective to describe the toxicity of nitrobenzene derivatives towards the same (Tripathi & Mishra, 2014). Further based on several energy descriptors, heat of formation, atomic charge, dipole moment and polarizability values, Bao et al. (2012) predicted the toxicity of several nitrobenzene derivatives towards the Scenedesmus obliguus. Very recently, Tawari et al. (2014) attempted to build QSTR model to describe the mutagenicity of nitroaromatic compounds. Pandith and Islam (2013) further built QSTR models by using log P, electrophilicity (ω) and LUMO energy to predict the acute toxicity of aliphatic compounds towards V. fischeri. HOMO energy, molecular weight, LUMO energy, chemical potential, zero point energy, and hardness were also used to model QSTR in order to predict 50% effective inhibition concentration of various substituted benzene derivatives towards the algae Scenedesmus obliquus (Haghdadi & Fatemi, 2010). Quite high correlation (r = 0.91) revealed that such properties can be properly described by using such molecular indices. Eroglu et al. (2007) used quantum-chemical descriptors like translational entropy, principal moment of inertia, the highest and the lowest vibrational wave numbers to predict the acute toxicity of several organic compounds towards Fathead Minnow. Further, toxicity of organophosphorus compounds was assessed using the regression models mady by different quantum-chemical and topological descriptors (Senior et al., 2011). Wiener index, Harary index, modified Randic index, Kupchik modified connectivity index (Todeschini & Con­sonni, 2000), electronegative Randic index, heteroatom corrected extended connectivity Randic index

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