Physicochemical and Topological Correlates of the Enzymatic Acetyltransfer Reaction

Basak, Subhash C.; Gieschen, D. P.; Harriss, Donald K.; Magnuson, Vincent R.

doi:10.1002/jps.2600720823

Cited by 34 publications

(12 citation statements)

References 19 publications

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“…These descriptors include Wiener number [26], the molecular connectivity () indices as calculated by Randic´ [27,28] and Kier and Hall [29], information theoretic indices defined on distance matrices of graphs using the methods of Bonchev and Trinajstic [30] as well as those of Raychaudhury et al [31], parameters defined on the neighborhood complexity of vertices in hydrogen-filled molecular graphs [32][33][34], Balaban's J indices [35][36][37], the local orthogonal vertex invariants (LOVIs) [38], kappa shape descriptors [39,40], and the electrotopological indices of Kier and Hall [41][42][43]. More information on the set of molecular descriptors calculated by POLLY has been reported in earlier studies [18,44,45].…”

Section: Calculation Of Topological Indicesmentioning

confidence: 99%

Optimal neighbor selection in molecular similarity: comparison of arbitraryversustailored prediction spaces†

Gute

Basak

2006

SAR and QSAR in Environmental Research

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Three classes of arbitrary quantitative molecular similarity analysis (QMSA) methods have been computed using atom pairs (APs), topological indices (TIs), and principal components (PCs) derived from topological indices. Tailored QMSA models have been developed from TIs selected through ridge regression. K-nearest neighbor (kNN) based estimation has been applied to all of the methods to estimate normal vapor pressure (p(vap)) and water solubility (sol) for a set of 194 chemicals. Results show that the tailored QMSA methods are superior to arbitrary similarity methods in estimating both of these properties for the given set of chemicals.

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Section: Calculation Of Topological Indicesmentioning

confidence: 99%

Optimal neighbor selection in molecular similarity: comparison of arbitraryversustailored prediction spaces†

Gute

Basak

2006

SAR and QSAR in Environmental Research

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“…It is interesting to observe that parameter p 3 gives, in most cases, useful correlations. This is not surprising in view of the pioneering work of Wienner on correlations of thermodynamical properties with total number of paths and paths of length 3.31 Previous studies of Basak et al 32 and have shown that lipophilic and steric parameters are crucial factors for the nonspecific mode of action of the barbiturate group of drugs. Descriptors which reflect the stereoelectronic makeup of a molecule, as molecular path sequences within a family of structurally related compounds, correlate very well with log P. The correlation coefficients from the regression equations with BR are also quite good.…”

Section: Application and Resultsmentioning

confidence: 67%

Comparison of sequences as a method for evaluation of the molecular similarity

Jerman-Blažič¹,

Fabiĉ²,

Randić³

1986

J Comput Chem

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String comparison techniques were developed and applied for measuring the molecular similarity of chemical structures. The molecular structures were encoded as a sequence of numbers representing counts of paths of different lengths. The similarity index between two compounds was calculated as the difference between the gains of information derived through comparison of the corresponding molecular path sequences. Ranks between the structures of the studied data base obtained according to this similarity were used as basic data for deriving correspondences between the elements of the set of compounds. The method was applied on a group of 41 barbiturates. Correlation equations were calculated for different groups of compounds grouped according to the displayed similarity. The correlation equations and the corresponding statistics were obtained using standard computer programs. Special algorithm for computing the similarity index and the correlation matrix (outlined very briefly) was developed and implemented on VAX 11/750.

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“…For instance, many software programs are now available such as Molconn-Z [5], Polly [6], DRAGON [7], and CODESSA [8] that calculate measurements of chemical structure. A past limitation existed when chemical activity data were available, but there were few structure measurements available to allow predictions of those activities.…”

Section: Introductionmentioning

confidence: 99%

Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

2015

Advances in Mathematical Chemistry and Applications

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A primary goal of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) is to predict chemical activities from chemical structure. Chemical structure can be quantified in many ways resulting in hundreds, if not thousands, of measurements for every chemical. Chemical activities measures how the chemical interacts with other chemicals, e.g. toxicity, biodegradability, boiling point, and vapor pressure. Typically there are more chemical structure measurements than chemicals being measured, the so-called large-p, smalln problem. Here we review some of the statistical procedures that have been commonly used to explore these problems in the past and provide several examples of their use. Finally, we peek into the future to discuss two areas that we believe will see dramatically increased attention in the near future: model averaging and Bayesian techniques.

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Physicochemical and Topological Correlates of the Enzymatic Acetyltransfer Reaction

Cited by 34 publications

References 19 publications

Optimal neighbor selection in molecular similarity: comparison of arbitraryversustailored prediction spaces†

Optimal neighbor selection in molecular similarity: comparison of arbitraryversustailored prediction spaces†

Comparison of sequences as a method for evaluation of the molecular similarity

Linear Regression, Model Averaging, and Bayesian Techniques for Predicting Chemical Activities from Structure

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