3D-Chiral quadratic indices of the ‘molecular pseudograph’s atom adjacency matrix’ and their application to central chirality codification: classification of ACE inhibitors and prediction of σ-receptor antagonist activities

Marrero-Ponce, Yovani; Dı́az, Humberto González; Zaldívar, Vicente Romero; Torrens, Francisco; Castro, Eduardo A.

doi:10.1016/j.bmc.2004.07.051

Cited by 86 publications

(78 citation statements)

References 24 publications

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“…U wobble pairs [20][21][22][23]. On the other hand, the general principles of the molecular quadratic indices of the "molecular pseudograph`s atom adjacent matrix" for small-to-medium sized organic compounds have been explained in some detail elsewhere [15][16][17][18][19]. However, this work gives an extended overview of this approach.…”

Section: Methodsmentioning

confidence: 99%

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Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region

Marrero-Ponce

Nodarse

González-Dı́az

et al. 2004

IJMS

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This report describes a new set of macromolecular descriptors of relevance to nucleic acid QSAR/QSPR studies, nucleic acids' quadratic indices. These descriptors are calculated from the macromolecular graph's nucleotide adjacency matrix. A study of the interaction of the antibiotic Paromomycin with the packaging region of the RNA present in type-1 HIV illustrates this approach. A linear discriminant function gave rise to excellent discrimination between 90.10% (91/101) and 81.82% (9/11) of interacting/noninteracting sites of nucleotides in training and test set, respectively. The LOO crossvalidation procedure was used to assess the stability and predictability of the model. Int. J. Mol. Sci. 2004, 5 277Using this approach, the classification model has shown a LOO global good classification of 91.09%. In addition, the model's overall predictability oscillates from 89.11% until 87.13%, when n varies from 2 to 3 in leave-n-out jackknife method. This value stabilizes around 88.12% when n was > 3. On the other hand, a linear regression model predicted the local binding affinity constants [log K (10 -4 M -1 )] between a specific nucleotide and the aforementioned antibiotic. The linear model explains almost 92% of the variance of the experimental log K (R = 0.96 and s = 0.07) and LOO press statistics evidenced its predictive ability (q 2 = 0.85 and s cv = 0.09). These models also permit the interpretation of the driving forces of the interaction process. In this sense, developed equations involve short-reaching (k ≤ 3), middle-reaching (4 < k < 9) and far-reaching (k = 10 or greater) nucleotide's quadratic indices. This situation points to electronic and topologic nucleotide's backbone interactions control of the stability profile of Paromomycin-RNA complexes. Consequently, the present approach represents a novel and rather promising way to chem & bioinformatics research.

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Section: Methodsmentioning

confidence: 99%

“…This point of view was very recently successfully applied to the prediction of physical properties and Caco-2 permeability of organic compounds and drugs, respectively [15][16][17][18]. Interestingly, molecular quadratic indices can be generalized to allow the codification of 3D-structural features [19].…”

Section: Introductionmentioning

confidence: 99%

Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region

Marrero-Ponce

Nodarse

González-Dı́az

et al. 2004

IJMS

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“…In the canonical basis, the coordinates of any vector X coincide with the components of this vector [30][31][32][33]. For this reason, those coordinates can be considered as weights (atom labels) of the vertices of the molecular pseudograph [15][16][17][18][19][20][21][22][23].…”

Section: Local (Atom) Linear Indices Of the "Molecular Pseudograph's mentioning

confidence: 99%

“…Here, M(G) = M = [a ij ], denotes the matrix of f k (x i ) with respect to the natural basis. In this matrix n is the number of vertices (atoms) of G and the elements a ij are defined as follows [15][16][17][18][19][20][21][22][23]:…”

Section: Local (Atom) Linear Indices Of the "Molecular Pseudograph's mentioning

confidence: 99%

“…More recently, the TOMOCOMD-CARDD approach has been applied to the fast-track experimental discovery of novel antimalarial compounds [21]. Codification of chirality and other 3D structural features constitutes another advantage of this method [22]. The latter opportunity has allowed the description of the significance-interpretation and the comparison to other molecular descriptors [16,23].…”

Section: Introductionmentioning

confidence: 99%

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Atom, Atom-Type, and Total Linear Indices of the “Molecular Pseudograph’s Atom Adjacency Matrix”: Application to QSPR/QSAR Studies of Organic Compounds

et al. 2004

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Abstract:In this paper we describe the application in QSPR/QSAR studies of a new group of molecular descriptors: atom, atom-type and total linear indices of the molecular pseudograph's atom adjacency matrix. These novel molecular descriptors were used for the prediction of boiling point and partition coefficient (log P), specific rate constant (log k), and antibacterial activity of 28 alkyl-alcohols and 34 derivatives of 2-furylethylenes, respectively. For this purpose two quantitative models were obtained to describe the alkyl-alcohols' boiling points. The first one includes only two total linear indices and showed a good behavior from a statistical point of view (R 2 = 0.984, s = 3.78, F = 748.57, linear discriminant model for classifying antibacterial activity of these compounds was also achieved with the use of the atom and atom-type linear indices. The global percent of good classification in training and external test set obtained was of 94.12% and 100.0%, respectively. The comparison with other approaches (connectivity indices, total and local spectral moments, quantum chemical descriptors, topographic indices and Estate/biomolecular encounter parameters) reveals a good behavior of our method. The approach described in this paper appears to be a very promising structural invariant, useful for QSPR/QSAR studies and computer-aided "rational" drug design.

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Molecular Descriptors for Biological Systems

Sukumar

Das

Krein

et al. 2011

Computational Approaches in Cheminformatics and Bioinformatics

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3D-Chiral quadratic indices of the ‘molecular pseudograph’s atom adjacency matrix’ and their application to central chirality codification: classification of ACE inhibitors and prediction of σ-receptor antagonist activities

Cited by 86 publications

References 24 publications

Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region

Nucleic Acid Quadratic Indices of the “Macromolecular Graph’s Nucleotides Adjacency Matrix”. Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 Ψ-RNA Packaging Region

Atom, Atom-Type, and Total Linear Indices of the “Molecular Pseudograph’s Atom Adjacency Matrix”: Application to QSPR/QSAR Studies of Organic Compounds

Molecular Descriptors for Biological Systems

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