We have carried out comprehensive studies on the physico–chemical properties of a large number of diversified compounds. In this respect, the applicability of the proposed index has been checked for a wide range of properties including: partition coefficient, molar refraction, molar volume, parachor, polarizability, standard enthalpy of formation, toxicity,boiling point, heat capacity, refractive index, and Gibbs free energy. On the other hand, our analysis was based on different category of compounds such as: alkanes, cycloalkanes, silicon/titanium halides, methyl halides, alcohols, aldehydes and ketones, carboxylic acids, as well as isomeric systems. The values of –index were computed for each compound with a view to study their correlation potential in developing **QSPR**/**QSAR** models.

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Present study shows the correlation of activity of as many as 13 Anti- bacterial (containing benzimidazole and beta-lactam moiety ) drugs in terms of global reactivity descriptors under paradigm of **QSPR**/**QSAR** study. Investigation of antimicrobial activity of the compounds was done by using Gram-positive (S. aureus, S. mutans and B. subtilis) bacteria. The global descriptors nicely correlate the variation of activity with structures of the drug molecules.

In the early 1960s, Corwin Hansch [1] extended the concept of Linear –free energy relations (LFER) to describe the effectiveness of biologically-active molecule. This represented efforts to quantitatively relate the structure of a compound to its activity and resulting equations were aptly named quantitative structure activity relationships (**QSAR**). Today, these equations are also called quantitatively structure property relationship (**QSPR**). Generating useful Hansch equation can be very challenging and even a good Hansch equation will not give perfect predication of activity. For this reason new methods have some what replaced the traditional Hansch analysis. In the late 1980s and early 1990s combinatorial chemistry emergent diminished the importance of **QSAR**. Since large libraries of compounds bearing varying substitutents could be easily prepared, being able to predict activity was no longer necessary, simply by making all the compounds one can imagine and test them in high-throughput screens.

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Topological indices are the real number of a molecular structure obtained using molecular graph G. Topological indices are used for **QSPR**, **QSAR** and structural design in chemistry, nanotechnology, and pharmacology. Moreover, physicochemical properties such as the boiling point, the enthalpy of vaporization, and stability can be estimated by **QSAR**/**QSPR** models. In this study, the **QSPR** (Quantitative Structure-Property Relationship) models were designed using the Gutman index, the product connectivity Banhatti index, the Variance of degree index, and the Sigma index to predict the thermodynamic properties of monocarboxylic acids. The relationship analyses between the thermodynamic properties and the topological indices were done by using the curvilinear regression method. It was used the linear, quadratic and cubic equations of the curvilinear regression model. These regression models were then compared.

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Abstract. Topological indices are numerical parameters of a graph which characterize its topol- ogy and are usually graph invariant. In **QSAR**/**QSPR** study, physico-chemical properties and topo- logical indices such as Randi´c, atom-bond connectivity ( ABC ) and geometric-arithmetic ( GA ) index are used to predict the bioactivity of chemical compounds. Graph theory has found a considerable use in this area of research. In this paper, we study and derive analytical closed results of general Randi´c index R α ( G ) with α = 1, 1 2 , − 1, − 1 2 , for boron triangular sheet BTS ( m, n ) , borophene chain of

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determine the extent and the rate of chemical’s absorption, distribution, metabolism and excretion. A typical PBPK model represents the body as anatomically and physiologically defined compartments consisting of a series of organs (liver, lungs, kidney, brain, heart) and tissues of interest (bone, fat, muscles, skin), dose and route of exposure, and major route of metabolism. The compartments are linked by blood flow and movement of chemical between compartments is determined by tissue/blood partitioning and blood flow rate. A series of differential mass balance equations are written for each tissue block and plasma, which represents change in drug concentration in tissues and plasma over time (Reddy et al., 2005). A typical dermal PBTK model may consider the skin as one, two or multi-compartmental unit. The PBPK models are suitable over traditional compartmental models in hypotheses generation and testing involving anatomical, physiological, and environmental change (van der Merwe et al., 2006). PBPK modeling is widely used in parallel to **QSAR** modeling and a large number of dermal PBPK models have been proposed in recent years (Corley et al., 2000; Poet et al., 2000; McCarley and Bunge, 2001; Thrall et al., 2002; van der Merwe et al., 2006; Kim et al., 2007; Norman et al., 2008).

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Cheminformatics is an emerging field in which quantitative structure-activity (**QSAR**) and Structure-property (**QSPR**) relationships predict the biological activities and properties of nanomaterial see [1-4]. In these studies, some physcio-chemical properties and topological indices are used to predict bioactivity of the chemical compounds see [5-7].

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Quantitative Structure - activity relationship and Quantitative Structure - property relationship (**QSAR**/ **QSPR**) studies are useful tools in the rational search for bioactive molecules. **QSPR** models are mathematical equations which attempt to correlate chemical structure to a wide variety of physical, chemical and biological properties 1 . (**QSPR**/**QSAR**) represents an attempt to relate structural descriptors of molecules with their physicochemical properties and biological activities 2 . Today, QSARs are being applied in many disciplines with much emphasis on drug design 3 . The lipophilicity expressed by the logarithmic partition coefficient (logP) is a very important physicochemical parameter which explains a partitioning equilibrium of solute molecules between water and an immiscible partitioning solvent 4-6 . Lipophilicity is a physicochemical property of principal importance in drug discovery and development 7 . The aim of the present study is to use topological indices for predicting lipophilicity (logP) of a series of 5-(2-Oxo-3-aryl-diazenyl-4-methyl-2H-chromen-8-yl)- 3-thio-1,2,4-triazoles. The use of topological indices is an important stage in **QSPR** studies. In the present work indicator parameter and topological indices used in the modeling of lipophilicity are O-atom, ZM1 (First Zagreb index M1) 8 , HI (Harary index) 9 and IDE (mean information content on the distance equality information indices) 10 . The aim of this study is to develop a **QSPR** model to correlate the structural features of this class of compounds with their lipophilicity (logP) using topological indices. In the present **QSPR** study, the topological indices and structural indicator are used as structural descriptors for 21derivatives of 5-(2-Oxo-3-aryl-diazenyl-4-methyl-2H- chromen-8-yl)-3-thio-1,2,4-triazoles 11 for modeling of lipophilicity.

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The structures of the selected fifty-five ketones were sketched and energy-minimized by SYBYL 8.1 [26] us- ing the Tripos Force Filed and Gasteiger-Hückel charges with a 0.05 kcal/(mol × Å) energy termination gradient, dielectric constant ε = 1.0, and an 8.0 Å nonbonded in- teraction (NB) cutoff. Molecular descriptors used for describing the acidity and generating the **QSPR** equation were calculated for each molecule using MDL **QSAR** version 2.2.0.365. The stepwise multiple linear regres- sion method was used to build the model. The number of descriptors (n) in the equation was limited to no more than the square root of the number of ketones in the TSET minus 2 (n ≤ (TSET) 0.5 – 2), which is 5 in this case.

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(**QSAR**/**QSPR**) are mathematical models obtain via statis- tical regression analysis aiming at predicting properties of molecules from their structure. Molecular activity and properties obtained experimentally are digital values but structures are in graphical form. Thus molecular topology involves the translation of molecular structure in to char- acteristic numerical descriptors which are known as topo- logical indices. In chemical graph theory and topology, atoms are treated as vertices and the bonds edges. When certain condition are imposed on vertices, edges, or both a number is obtain which is called the topological index used in the modeling of physicochemical properties, bio- logical activities and toxicity of organic compounds. 9-15

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Abstract: In the study of **QSAR**/**QSPR**, due to high degree of predictability of pharmaceutical properties, the eccentric-connectivity index has very important place among the other topological descriptors, In this paper, we compute the exact formulas of eccentric-connectivity index and its corresponding polynomial, total eccentric-connectivity index and its corresponding polynomial, first Zagreb eccentricity index, augmented eccentric-connectivity index, modified eccentric-connectivity index and its corresponding polynomial for a class of phosphorus containing dendrimers.

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results and discussion made above conclusion may be drawn - no single molecular descriptors or physicochemical properties could be used individually for **QSAR** studies. In multivariate correlations more than the graph theoretical descriptors the physicochemical properties have shown significant correlation. Thus, physicochemical property studies are most suited for understanding **QSAR** of anthracyclines.

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Values of the descriptors of the Angiotensin II Antagonist derivatives have been calculated using PM3 method and are given in table-2. With the help of these values of descriptors, six **QSAR** models have been developed using MLR analysis in different combinations of descriptors. The Chemical Potential (µ) and Absolute Hardness (ƞ) descriptors have no predicting power and hence not included in the models. Best **QSAR** models is the model sixth listed below-

Biological activity data and various physico-chemical parameters were taken as dependent and independent variables and correlations were established using PLS method. When the compounds were subjected to under goes PLS method to developed **QSAR** models by using step wise forward-backward variable selection mode, four **QSAR** models, Model-I and Model-II, Model-III were developed for both the methods respectively as shown below and other good model predicted activity shown abstract.

ABSTRACT: In the present paper **QSAR** studies related to some novel substituted pyrazole derivatives are reported. All the compounds were evaluated for antmicrobial activity viz- antifungal activity against A. niger. Their reported antimicrobial activities were used for Quantitative Structure Activity Relationship (**QSAR**) studies to find correlation between different calculated molecular descriptor of the compounds and biological activity. It is reported that five compounds viz. A-2, A-6, A-7, A-10 and A-11 showed maximum activity against A. niger .

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Pharmacophore modeling is an important approach to quantitatively search common chemical features among a number of structures. A qualified pharmacophore model can be used as a query for searching chemical databases to find out new chemical moieties. Pharmacophore modeling also correlates bio- activities with the spatial arrangement of various chemical features 1 . Pharmacophore mapping 2 , a ligand- based drug design approach, and quantitative structure-activity relationship (**QSAR**) can be utilized in drug discovery in different ways like rationalization of activity trends in compounds under study, prediction of the activity of novel molecules, database search studies in search of new hits and to identify important features for activity 3-6 .

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In this presented work we have indentified the important structural requirement of alpha keto amides for inhibition of GP -120. Four different **QSAR** models are generated by using MLR technique; two models are selected on the basis of all statistical coefficients. The both the models are showing similar results which can be use full for the designing of more potent GP-120 inhibitors.

The contribution plot and 3D – **QSAR** graphical interface provide with the point generated in the model were E_143, H_87, S_193 accounting for electrostatic, hydrophobic, and steric fields at the lattice points on the grid. These points suggest the significance and requirements of the these properties in the structure to maximize the anti-microbial activity. There is less significant difference in the actual and predicted activity that provides with good predictive ability of the **QSAR** tool model. This can be observed from the fitness plot.

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The **QSPR** analysis provides a significant structural insight into the physiochemical properties of butane derivatives. We study some physiochemical properties of fourteen butane derivatives and develop a **QSPR** model using four topological indices and butane derivatives. Here we analyze how closely the topological indices are related to the physiochemical properties of butane derivatives. For this we compute analytically the topological indices of butane derivatives and plot the graphs between each of these topological indices to the properties of butane derivatives using Origin. This **QSPR** model exhibits a close correlation between Heavy atomic count, Complexity, Hydrogen bond acceptor count, and Surface tension of butane derivatives with the Redefined first Zagreb index, the Redefined third Zagreb index, the Sum connectivity index and the Reformulated first Zagreb index, respectively.

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