Quantitative structure-activity relationship indices

The CHI index is reportedly a relevant parameter in quantitative structure-activity relationship (QSAR) studies [41]. With this approach, log P could be determined in the range -0.45more than 25000 compounds with excellent reproducibility (within 2 index units) and reported in a GlaxoSmithKline database [11]. Two main drawbacks were identified using this approach (i) the assumptions used in Ref [7], i.e. that S is constant for all compounds and that the system dwell volume is excluded in calculations, yield some discrepancies in the resulting log P, and (ii) the set of gradient calibration... 

US Environmental Protection Agency [USEPA]. 1994. USEPA/EC joint project on the evaluation of (quantitative) structure activity relationships. Washington (DC) US Environmental Protection Agency. http //intranet.epa.gov/oppthome/testsite/MPD-SAR/index.html (accessed December 28, 2007). 

The next step was made by Klebe et al. [50]. Two 3D-QSAR methods were applied to get three-dimensional quantitative structure-activity relationships using a training set of 72 inhibitors of the benzamidine type with respect to their binding affinities toward Factor Xa to yield statistically reliable models of good predictive power [51-54] the widely used CoMFA method (for steric and electrostatic properties) and the comparative molecular similarity index analysis (CoMSlA) method (for steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor properties). These methods allowed the consideration of various physicochemical properties, and the resulting contribution maps could be intuitively interpreted. 

L. H. Hall, B. Mohney and L. B. Kier, The electrotopological state-an atom index for QSAR., Quantitative Structure-Activity Relationships, 1991, 10, 43-51. 

Gombar, V.K., Kumar, A. and Murthy, M.S. (1987b). Quantitative Structure-Activity Relationships Part IX - A Modified Connectivity Index as Structure Quantifier. Indian J.Chem., 26B, 1168-1170. 

The dectrotopological state index (Si) is an extension of the purely topological index [55]. Electronic properties (i.e., the charge distribution in the molecule) are also considered. Atoms with identical T may differ in their Si. Electrotopological state indices have been used successfully to predict physico-chemical data, such as basicity (pJCa) or Hpophilicity (logP), as well as for quantitative structure-activity relationship (QSAR) or QSPR studies. 

Quantitative Structure Activity Relationships (QSAR) and Modeling Society. URL http //www.ndsu.nodak.edu/qsar soc/index.htm. Classical QSAR, multivariate statistical modeling, molecular modeling, computer-aided drug design, and environmental chemistry. 

Other pharmacological activities have also been correlated with quantum-chemically derived descriptors. For instance, the quantitative structure-activity relationship developed for the antibacterial activity of a series of monocyclic (i-lactam antibiotics included the atomic charges, the bond orders, the dipole moment, and the first excitation energy of the compound [103]. The fungicidal activity of A3-l,2,4-thiadiazolines has been correlated with an index of frontier orbital electron density derived from semi-empirical PM3 molecular orbital calculations [104],... 

Three major approaches to the prediction of aqueous solubility of organic chemicals using Quantitative Structure Activity Relationship (QSAR) techniques arc reviewed. The rationale behind six QSAR models derived from these three approaches, and the quality of their fit to the experimental data are summarized. Their utility and predictive ability are examined and compared on a common basis. Three of the models employed octanol-water partition coefficient as the primary descriptor, while two others used the solvatochromic parameters. The sixth model utilized a combination of connectivity indexes and a modified polarizability parameter. Considering the case of usage, predictive ability, and the range of applicability, the model derived from the connectivity- polarizability approach appears to have greater utility value. 

Molecular Similarity and QSAR. - In a first contribution on the design of a practical, fast and reliable molecular similarity index Popelier107 proposed a measure operating in an abstract space spanned by properties evaluated at BCPs, called BCP space. Molecules are believed to be represented compactly and reliably in BCP space, as this space extracts the relevant information from the molecular ab initio wave functions. Typical problems of continuous quantum similarity measures are hereby avoided. The practical use of this novel method is adequately illustrated via the Hammett equation for para- and me/a-substituted benzoic acids. On the basis of the author s definition of distances between molecules in BCP space, the experimental sequence of acidities determined by the well-known a constant of a set of substituted congeners is reproduced. Moreover, the approach points out where the common reactive centre of the molecules is. The generality and feasibility of this method will enable predictions in medically related Quantitative Structure Activity Relationships (QSAR). This contribution combines the historically disparate fields of molecular similarity and QSAR. 

Hall L H, B Mohney and L B Kier 1991. The Electrotopological State An Atom Index for QSAR Quantitative Structure-Activity Relationships 10 43-51. 

In many cases, at least for screening purposes and for preliminary comparisons of several compounds, approximate information on the intrinsic stability of a molecule, taken as an index of persistence potential that is independent of environmental variables, can be useful. In these cases the use of predictive approaches based on the molecular properties and structure (QSAR quantitative structure-activity relationships) could be very helpful in the absence of experimental information. Although the application of QSARs for the prediction of persistence has not yet been developed for screening as it has for other ecotoxicological aspects (e.g. prediction of toxic effects or bioaccumulation), in the last few years there has been some promising progress (Tremolada et al, 1991 Vasseur etal., 1993 Macalady and Schwarzenbach, 1993). 

In recent years there has been a notable increase in research on structure-activity relationships (SARs), also called quantitative structure-activity relationships (QSARs), used to assess the toxicity of substances for which there are few experimental data. This approach involves establishing mathematical relationships derived from computer modeling, based on known toxicity data of similar (or dissimilar) types of compounds, octanol-water partition coefficients, molar connectivity index values, and other parameters. A detailed discussion on this subject is beyond the scope of this book. 

QSAR, as the acronym implies, is about quantitative structure-activity relationships and not about quantitative mechanistic-activity relationships We will comment on few of the errors on the list of 21 types of error, which, in our view, are not errors and on a few questions for which have been offered answers for those interested in considering them. It is not our aim here to critically review the paper How Not to Develop a Quantitative Structure-Activity or Structure-Property Relationship by Dearden et al but to make our contribution to How to Develop a Quantitative Structure-Activity or Structure-Property Relationship. One particular matter that we will address is, in our view, a misrepresentation in the article by Dearden et al. of the connectivity index and the variable connectivity index. 

Besides the applications of the electrophilicity index mentioned in the review article [40], following recent applications and developments have been observed, including relationship between basicity and nucleophilicity [64], 3D-quantitative structure activity analysis [65], Quantitative Structure-Toxicity Relationship (QSTR) [66], redox potential [67,68], Woodward-Hoffmann rules [69], Michael-type reactions [70], Sn2 reactions [71], multiphilic descriptions [72], etc. Molecular systems include silylenes [73], heterocyclohexanones [74], pyrido-di-indoles [65], bipyridine [75], aromatic and heterocyclic sulfonamides [76], substituted nitrenes and phosphi-nidenes [77], first-row transition metal ions [67], triruthenium ring core structures [78], benzhydryl derivatives [79], multivalent superatoms [80], nitrobenzodifuroxan [70], dialkylpyridinium ions [81], dioxins [82], arsenosugars and thioarsenicals [83], dynamic properties of clusters and nanostructures [84], porphyrin compounds [85-87], and so on. 

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