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Yovani Marrero Ponce - One of the best experts on this subject based on the ideXlab platform.
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linear indices of the macromolecular graph s nucleotides Adjacency Matrix as a promising approach for bioinformatics studies part 1 prediction of paromomycin s affinity constant with hiv 1 ψ rna packaging region
Bioorganic & Medicinal Chemistry, 2005Co-Authors: Yovani Marrero Ponce, Juan Alberto Castillo Garit, Delvin NodarseAbstract:Abstract The design of novel anti-HIV compounds has now become a crucial area for scientists around the world. In this paper a new set of macromolecular descriptors (that are calculated from the macromolecular graph’s nucleotide Adjacency Matrix) of relevance to nucleic acid QSAR/QSPR studies, nucleic acids’ linear indices. A study of the interaction of the antibiotic Paromomycin with the packaging region of the HIV-1 Ψ-RNA has been performed as example of this approach. A multiple 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 more than 87% of the variance of the experimental Log K (R = 0.93 and s = 0.102 × 10−4 M−1) and leave-one-out press statistics evidenced its predictive ability (q2 = 0.82 and scv = 0.108 × 10−4 M−1). The comparison with other approaches (macromolecular quadratic indices, Markovian Negentropies and ‘stochastic’ spectral moments) reveals a good behavior of our method.
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protein quadratic indices of the macromolecular pseudograph s α carbon atom Adjacency Matrix 1 prediction of arc repressor alanine mutant s stability
Molecules, 2004Co-Authors: Yovani Marrero Ponce, Vicente Romero Zaldivar, Ricardo Medina Marrero, Eduardo A Castro, Ronal Ramos De Armas, Humberto Gonzalez Diaz, Francisco TorrensAbstract:Abstract : This report describes a new set of macromolecular descriptors of relevance to protein QSAR/QSPR studies, protein’s quadratic indices. These descriptors are calculated from the macromolecular pseudograph’s α-carbon atom Adjacency Matrix. A study of the protein stability effects for a complete set of alanine substitutions in Arc repressor illustrates this approach. Quantitative Structure-Stability Relationship (QSSR) models allow discriminating between near wild-type stability and reduced-stability A-mutants. A linear discriminant function gives rise to excellent discrimination between 85.4% (35/41) and 91.67% (11/12) of near wild-type stability/reduced stability mutants in training and test series, respectively. The model’s overall predictability oscillates from 80.49 until 82.93, when n varies from 2 to 10 in leave- n -out cross validation procedures. This value stabilizes around 80.49% when n was > 6. Additionally, canonical regression analysis corroborates the statistical quality of the classification model (Rcanc = 0.72,
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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
Molecules, 2004Co-Authors: Yovani Marrero Ponce, Vicente Romero Zaldivar, Francisco Torrens, Juan Alberto Castillo Garit, Eduardo A CastroAbstract:In this paper we describe the application in QSPR/QSAR studies of a newgroup of molecular descriptors: atom, atom-type and total linear indices of the molecularpseudograph’s atom Adjacency Matrix. These novel molecular descriptors were used forthe prediction of boiling point and partition coefficient (log P), specific rate constant (logk), and antibacterial activity of 28 alkyl-alcohols and 34 derivatives of 2-furylethylenes,respectively. For this purpose two quantitative models were obtained to describe thealkyl-alcohols’ boiling points. The first one includes only two total linear indices andshowed a good behavior from a statistical point of view (R2 = 0.984, s = 3.78, F = 748.57,q2 = 0.981, and scv = 3.91). The second one includes four variables [3 global and 1 local(heteroatom) linear indices] and it showed an improvement in the description of physicalproperty (R2 = 0.9934, s = 2.48, F = 871.96, q2 = 0.990, and scv = 2.79). Later, linearmultiple regression analysis was also used to describe log P and log k of the 2-furyl-ethylenes derivatives. These models were statistically significant [(R2 = 0.984, s = 0.143, and F = 113.38) and (R2 = 0.973, s = 0.26 and F = 161.22), respectively] and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment [(q2 = 0.93.8 and scv = 0.178) and (q2 = 0.948 and scv = 0.33), respectively]. Finally, a 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 E- state/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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total and local quadratic indices of the molecular pseudograph s atom Adjacency Matrix application to prediction of caco 2 permeability of drugs
International Journal of Molecular Sciences, 2003Co-Authors: Yovani Marrero Ponce, Vicente Romero Zaldivar, Miguel Angel Cabrera Perez, Ernest Ofori, Luis A MonteroAbstract:The high interest in the prediction of the intestinal absorption for New Chemical Entities (NCEs) is generated by the increasing rate in the synthesis of compounds by combinatorial chemistry and the extensive cost of the traditional evaluation methods. Quantitative Structure–Permeability Relationships (QSPerR) of the intestinal permeability across the Caco-2 cells monolayer (PCaco-2) could be obtained by the application of new molecular descriptors. In this sense, quadratic indices of the “molecular pseudograph’s atom Adjacency Matrix” and multiple linear regression analysis were used to obtain good quantitative models to determine the PCaco-2. QSPerR models found are significant from a statistical point of view. The total and local quadratic indices were calculated with the TOMO-COMD software. A leave-one-out cross-validation procedure (internal validation) and the evaluation of external test set of 20 drugs (external validation) revealed that regression models had a good predictive power. A comparison with results derived from other theoretical studies shown a quite satisfactory behavior of the present method. The descriptors included in the prediction models permitted the interpretation in structural terms of the permeability process, evidencing the main role of H-bonding and size properties. The models found were used in virtual screening of drug intestinal permeability and a relationship between PCaco-2 calculated and percentage of human intestinal absorption for the 72 compounds was established. These results suggest that the proposed method is able to predict PCaco-2, being a good tool for screening of PCaco-2 for large sets of NCEs synthesized via combinatorial chemistry approach.
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total and local quadratic indices of the molecular pseudograph s atom Adjacency Matrix applications to the prediction of physical properties of organic compounds
Molecules, 2003Co-Authors: Yovani Marrero PonceAbstract:A novel topological approach for obtaining a family of new molecular descriptors is proposed. In this connection, a vector space E (molecular vector space), whose elements are organic molecules, is defined as a “direct sum“ of different ℜi spaces. In this way we can represent molecules having a total of i atoms as elements (vectors) of the vector spaces ℜi (i=1, 2, 3,..., n; where n is number of atoms in the molecule). In these spaces the components of the vectors are atomic properties that characterize each kind of atom in particular. The total quadratic indices are based on the calculation of mathematical quadratic forms. These forms are functions of the k-th power of the molecular pseudograph's atom Adjacency Matrix (M). For simplicity, canonical bases are selected as the quadratic forms' bases. These indices were generalized to “higher analogues“ as number sequences. In addition, this paper also introduces a local approach (local invariant) for molecular quadratic indices. This approach is based mainly on the use of a local Matrix [Mk(G, FR)]. This local Matrix is obtained from the k-th power (Mk(G)) of the atom Adjacency Matrix M. Mk(G, FR) includes the elements of the fragment of interest and those that are connected with it, through paths of length k. Finally, total (and local) quadratic indices have been used in QSPR studies of four series of organic compounds. The quantitative models found are significant from a statistical point of view and permit a clear interpretation of the studied properties in terms of the structural features of molecules. External prediction series and cross-validation procedures (leave-one-out and leave-group-out) assessed model predictability. The reported method has shown similar results, compared with other topological approaches. The results obtained were the following: a) Seven physical properties of 74 normal and branched alkanes (boiling points, molar volumes, molar refractions, heats of vaporization, critical temperatures, critical pressures and surface tensions) were well modeled (R>0.98, q2>0.95) by the total quadratic indices. The overall MAE of 5-fold cross-validation were of 2.11 oC, 0.53 cm3, 0.032 cm3, 0.32 KJ/mol, 5.34 oC, 0.64 atm, 0.23 dyn/cm for each property, respectively; b) boiling points of 58 alkyl alcohols also were well described by the present approach; in this sense, two QSPR models were obtained; the first one was developed using the complete set of 58 alcohols [R=0.9938, q2=0.986, s=4.006oC, overall MAE of 5-fold cross-validation=3.824 oC] and the second one was developed using 29 compounds as a training set [R=0.9979, q2=0.992, s=2.97 oC, overall MAE of 5-fold cross-validation=2.580 oC] and 29 compounds as a test set [R=0.9938, s=3.17 oC]; c) good relationships were obtained for the boiling points property (using 80 and 26 cycloalkanes in the training and test sets, respectively) using 2 and 5 total quadratic indices: [Training set: R=0.9823 (q2=0.961 and overall MAE of 5-fold crossvalidation= 6.429 oC) and R=0.9927 (q2=0.977 and overall MAE of 5-fold crossvalidation= 4.801 oC); Test set: R=0.9726 and R=0.9927] and d) the linear model developed to describe the boiling points of 70 organic compounds containing aromatic rings has shown good statistical features, with a squared correlation coefficient (R2) of 0.981 (s=7.61 oC). Internal validation procedures (q2=0.9763 and overall MAE of 5-fold cross-validation=7.34 oC) allowed the predictability and robustness of the model found to be assessed. The predictive performance of the obtained QSPR model also was tested on an extra set of 20 aromatic organic compounds (R=0.9930 and s=7.8280 oC). The results obtained are valid to establish that these new indices fulfill some of the ideal requirements proposed by Randic for a new molecular descriptor.
Sirish L Shah - One of the best experts on this subject based on the ideXlab platform.
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root cause diagnosis of plant wide oscillations using the concept of Adjacency Matrix
Journal of Process Control, 2009Co-Authors: Hailei Jiang, Rohit S Patwardhan, Sirish L ShahAbstract:Abstract Oscillations are a common type of plant-wide disturbances whose effects propagate to many units and thus may impact overall process performance. It is important to detect and diagnose such oscillations early in order to rectify the situation. Many frequency domain tools such as the power spectrum and spectrum envelope methods are capable of detecting the oscillation frequency. However, few methods are available for locating the root cause which is the main objective of oscillation diagnosis. This paper proposes a new method to diagnose the root cause of plant-wide oscillations using the Adjacency Matrix. A novel feature of the new method is that it utilizes the information in the process flowsheet. The method is not data-based and it can be carried out without using any data. However, this method complements the data based methods very well and it is best used in combination with other data-based methods to provide powerful diagnosis of plant-wide oscillations. This paper is the subject of a newly proposed complete procedure for detection and diagnosis of plant-wide oscillation. Two industrial case studies are also presented to demonstrate the applicability of the proposed procedure.
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root cause diagnosis of plant wide oscillations using the Adjacency Matrix
IFAC Proceedings Volumes, 2008Co-Authors: Hailei Jiang, Rohit S Patwardhan, Sirish L ShahAbstract:Abstract Oscillations are a common type of plant-wide disturbances whose effects propagate to many units and thus may impact overall process performance. It is important to detect and diagnose such oscillations early in order to rectify the situation. Many frequency domain tools such as the power spectrum and spectrum envelope methods are capable of detecting the oscillation frequency. However, few methods are available for locating the root cause which is the main objective of oscillation diagnosis. This paper proposes a new method to diagnose the root cause of plant-wide oscillations using the Adjacency Matrix. A novel feature of the new method is that it utilizes the information in the process flowsheet. The method is not data-based and it can be carried out without using any data. However this method complements the data based methods very well and it is best used in combination with other data-based methods to provide powerful diagnosis of plant-wide oscillations. This paper is the subject of a newly proposed complete procedure for detection and diagnosis of plant-wide oscillation. Two industrial case studies are also presented to demonstrate the applicability of the proposed procedure.
Bojan Mohar - One of the best experts on this subject based on the ideXlab platform.
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hermitian Adjacency Matrix of digraphs and mixed graphs
Journal of Graph Theory, 2017Co-Authors: Bojan MoharAbstract:The article gives a thorough introduction to spectra of digraphs via its Hermitian Adjacency Matrix. This Matrix is indexed by the vertices of the digraph, and the entry corresponding to an arc from x to y is equal to the complex unity i (and its symmetric entry is −i) if the reverse arc yx is not present. We also allow arcs in both directions and unoriented edges, in which case we use 1 as the entry. This allows to use the definition also for mixed graphs. This Matrix has many nice properties; it has real eigenvalues and the interlacing theorem holds for a digraph and its induced subdigraphs. Besides covering the basic properties, we discuss many differences from the properties of eigenvalues of undirected graphs and develop basic theory. The main novel results include the following. Several surprising facts are discovered about the spectral radius; some consequences of the interlacing property are obtained; operations that preserve the spectrum are discussed—they give rise to a large number of cospectral digraphs; for every 0≤α≤3, all digraphs whose spectrum is contained in the interval (−α,α) are determined.
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hermitian Adjacency Matrix of digraphs and mixed graphs
arXiv: Combinatorics, 2015Co-Authors: Bojan MoharAbstract:The paper gives a thorough introduction to spectra of digraphs via its Hermitian Adjacency Matrix. This Matrix is indexed by the vertices of the digraph, and the entry corresponding to an arc from $x$ to $y$ is equal to the complex unity $i$ (and its symmetric entry is $-i$) if the reverse arc $yx$ is not present. We also allow arcs in both directions and unoriented edges, in which case we use $1$ as the entry. This allows to use the definition also for mixed graphs. This Matrix has many nice properties; it has real eigenvalues and the interlacing theorem holds for a digraph and its induced subdigraphs. Besides covering the basic properties, we discuss many differences from the properties of eigenvalues of undirected graphs and develop basic theory. The main novel results include the following. Several surprising facts are discovered about the spectral radius; some consequences of the interlacing property are obtained; operations that preserve the spectrum are discussed -- they give rise to an incredible number of cospectral digraphs; for every $0\le\alpha\le\sqrt{3}$, all digraphs whose spectrum is contained in the interval $(-\alpha,\alpha)$ are determined.
Konstantin Avrachenkov - One of the best experts on this subject based on the ideXlab platform.
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spectral analysis of the Adjacency Matrix of random geometric graphs
arXiv: Spectral Theory, 2019Co-Authors: Mounia Hamidouche, Laura Cottatellucci, Konstantin AvrachenkovAbstract:In this article, we analyze the limiting eigenvalue distribution (LED) of random geometric graphs (RGGs). The RGG is constructed by uniformly distributing $n$ nodes on the $d$-dimensional torus $\mathbb{T}^d \equiv [0, 1]^d$ and connecting two nodes if their $\ell_{p}$-distance, $p \in [1, \infty]$ is at most $r_{n}$. In particular, we study the LED of the Adjacency Matrix of RGGs in the connectivity regime, in which the average vertex degree scales as $\log\left( n\right)$ or faster, i.e., $\Omega \left(\log(n) \right)$. In the connectivity regime and under some conditions on the radius $r_{n}$, we show that the LED of the Adjacency Matrix of RGGs converges to the LED of the Adjacency Matrix of a deterministic geometric graph (DGG) with nodes in a grid as $n$ goes to infinity. Then, for $n$ finite, we use the structure of the DGG to approximate the eigenvalues of the Adjacency Matrix of the RGG and provide an upper bound for the approximation error.
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spectral analysis of the Adjacency Matrix of random geometric graphs
Allerton Conference on Communication Control and Computing, 2019Co-Authors: Mounia Hamidouche, Laura Cottatellucci, Konstantin AvrachenkovAbstract:In this article, we analyze the limiting eigen-value distribution (LED) of random geometric graphs (RGGs). The RGG is constructed by uniformly distributing n nodes on the d-dimensional torus $\Gamma^{d}\equiv [0, 1]^{d}$ and connecting two nodes if their $\ell_{p}$-distance, $ p\in [1,\ \infty]$ is at most r n . In particular, we study the LED of the Adjacency Matrix of RGGs in the connectivity regime, in which the average vertex degree scales as $\log(n)$ or faster, i.e., $\Omega(\log(n))$. In the connectivity regime and under some conditions on the radius r n , we show that the LED of the Adjacency Matrix of RGGs converges to the LED of the Adjacency Matrix of a deterministic geometric graph (DGG) with nodes in a grid as n goes to infinity. Then, for n finite, we use the structure of the DGG to approximate the eigenvalues of the Adjacency Matrix of the RGG and provide an upper bound for the approximation error. Index Terms--Random geometric graphs, Adjacency Matrix, limiting eigenvalue distribution, Levy distance.
Vicente Romero Zaldivar - One of the best experts on this subject based on the ideXlab platform.
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bond based linear indices of the non stochastic and stochastic edge Adjacency Matrix 1 theory and modeling of chemphys properties of organic molecules
Molecular Diversity, 2010Co-Authors: Yovani Marreroponce, Eugenio R Martinezalbelo, Gerardo M Casanolamartin, Juan A Castillogarit, Yunaimy Echeveriadiaz, Vicente Romero Zaldivar, Jan Tygat, Jose Rodriguez E BorgesAbstract:Novel bond-level molecular descriptors are proposed, based on linear maps similar to the ones defined in algebra theory. The kth edge-Adjacency Matrix (E k ) denotes the Matrix of bond linear indices (non-stochastic) with regard to canonical basis set. The kth stochastic edge-Adjacency Matrix, ES k , is here proposed as a new molecular representation easily calculated from E k . Then, the kth stochastic bond linear indices are calculated using ES k as operators of linear transformations. In both cases, the bond-type formalism is developed. The kth non-stochastic and stochastic total linear indices are calculated by adding the kth non-stochastic and stochastic bond linear indices, respectively, of all bonds in molecule. First, the new bond-based molecular descriptors (MDs) are tested for suitability, for the QSPRs, by analyzing regressions of novel indices for selected physicochemical properties of octane isomers (first round). General performance of the new descriptors in this QSPR studies is evaluated with regard to the well-known sets of 2D/3D MDs. From the analysis, we can conclude that the non-stochastic and stochastic bond-based linear indices have an overall good modeling capability proving their usefulness in QSPR studies. Later, the novel bond-level MDs are also used for the description and prediction of the boiling point of 28 alkyl-alcohols (second round), and to the modeling of the specific rate constant (log k), partition coefficient (log P), as well as the antibacterial activity of 34 derivatives of 2-furylethylenes (third round). The comparison with other approaches (edge- and vertices-based connectivity indices, total and local spectral moments, and quantum chemical descriptors as well as E-state/biomolecular encounter parameters) exposes a good behavior of our method in this QSPR studies. Finally, the approach described in this study appears to be a very promising structural invariant, useful not only for QSPR studies but also for similarity/diversity analysis and drug discovery protocols.
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non stochastic and stochastic linear indices of the molecular pseudograph s atom Adjacency Matrix a novel approach for computational in silico screening and rational selection of new lead antibacterial agents
Journal of Molecular Modeling, 2006Co-Authors: Yovani Marreroponce, Vicente Romero Zaldivar, Ricardo Medina Marrero, Eduardo A Castro, Francisco Torrens, Yamile Martinez, Milagros Garcia Bernal, Ricardo Grau AbaloAbstract:A novel approach (TOMOCOMD-CARDD) to computer-aided “rational” drug design is illustrated. This approach is based on the calculation of the non-stochastic and stochastic linear indices of the molecular pseudograph’s atom-Adjacency Matrix representing molecular structures. These TOMOCOMD-CARDD descriptors are introduced for the computational (virtual) screening and “rational” selection of new lead antibacterial agents using linear discrimination analysis. The two structure-based antibacterial-activity classification models, including non-stochastic and stochastic indices, classify correctly 91.61% and 90.75%, respectively, of 1525 chemicals in training sets. These models show high Matthews correlation coefficients (MCC=0.84 and 0.82). An external validation process was carried out to assess the robustness and predictive power of the model obtained. These QSAR models permit the correct classification of 91.49% and 89.31% of 505 compounds in an external test set, yielding MCCs of 0.84 and 0.79, respectively. The TOMOCOMD-CARDD approach compares satisfactorily with respect to nine of the most useful models for antimicrobial selection reported to date. Finally, an in silico screening of 87 new chemicals reported in the anti-infective field with antibacterial activities is developed showing the ability of the TOMOCOMD-CARDD models to identify new lead antibacterial compounds.
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protein quadratic indices of the macromolecular pseudograph s α carbon atom Adjacency Matrix 1 prediction of arc repressor alanine mutant s stability
Molecules, 2004Co-Authors: Yovani Marrero Ponce, Vicente Romero Zaldivar, Ricardo Medina Marrero, Eduardo A Castro, Ronal Ramos De Armas, Humberto Gonzalez Diaz, Francisco TorrensAbstract:Abstract : This report describes a new set of macromolecular descriptors of relevance to protein QSAR/QSPR studies, protein’s quadratic indices. These descriptors are calculated from the macromolecular pseudograph’s α-carbon atom Adjacency Matrix. A study of the protein stability effects for a complete set of alanine substitutions in Arc repressor illustrates this approach. Quantitative Structure-Stability Relationship (QSSR) models allow discriminating between near wild-type stability and reduced-stability A-mutants. A linear discriminant function gives rise to excellent discrimination between 85.4% (35/41) and 91.67% (11/12) of near wild-type stability/reduced stability mutants in training and test series, respectively. The model’s overall predictability oscillates from 80.49 until 82.93, when n varies from 2 to 10 in leave- n -out cross validation procedures. This value stabilizes around 80.49% when n was > 6. Additionally, canonical regression analysis corroborates the statistical quality of the classification model (Rcanc = 0.72,
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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
Molecules, 2004Co-Authors: Yovani Marrero Ponce, Vicente Romero Zaldivar, Francisco Torrens, Juan Alberto Castillo Garit, Eduardo A CastroAbstract:In this paper we describe the application in QSPR/QSAR studies of a newgroup of molecular descriptors: atom, atom-type and total linear indices of the molecularpseudograph’s atom Adjacency Matrix. These novel molecular descriptors were used forthe prediction of boiling point and partition coefficient (log P), specific rate constant (logk), and antibacterial activity of 28 alkyl-alcohols and 34 derivatives of 2-furylethylenes,respectively. For this purpose two quantitative models were obtained to describe thealkyl-alcohols’ boiling points. The first one includes only two total linear indices andshowed a good behavior from a statistical point of view (R2 = 0.984, s = 3.78, F = 748.57,q2 = 0.981, and scv = 3.91). The second one includes four variables [3 global and 1 local(heteroatom) linear indices] and it showed an improvement in the description of physicalproperty (R2 = 0.9934, s = 2.48, F = 871.96, q2 = 0.990, and scv = 2.79). Later, linearmultiple regression analysis was also used to describe log P and log k of the 2-furyl-ethylenes derivatives. These models were statistically significant [(R2 = 0.984, s = 0.143, and F = 113.38) and (R2 = 0.973, s = 0.26 and F = 161.22), respectively] and showed very good stability to data variation in leave-one-out (LOO) cross-validation experiment [(q2 = 0.93.8 and scv = 0.178) and (q2 = 0.948 and scv = 0.33), respectively]. Finally, a 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 E- state/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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total and local quadratic indices of the molecular pseudograph s atom Adjacency Matrix application to prediction of caco 2 permeability of drugs
International Journal of Molecular Sciences, 2003Co-Authors: Yovani Marrero Ponce, Vicente Romero Zaldivar, Miguel Angel Cabrera Perez, Ernest Ofori, Luis A MonteroAbstract:The high interest in the prediction of the intestinal absorption for New Chemical Entities (NCEs) is generated by the increasing rate in the synthesis of compounds by combinatorial chemistry and the extensive cost of the traditional evaluation methods. Quantitative Structure–Permeability Relationships (QSPerR) of the intestinal permeability across the Caco-2 cells monolayer (PCaco-2) could be obtained by the application of new molecular descriptors. In this sense, quadratic indices of the “molecular pseudograph’s atom Adjacency Matrix” and multiple linear regression analysis were used to obtain good quantitative models to determine the PCaco-2. QSPerR models found are significant from a statistical point of view. The total and local quadratic indices were calculated with the TOMO-COMD software. A leave-one-out cross-validation procedure (internal validation) and the evaluation of external test set of 20 drugs (external validation) revealed that regression models had a good predictive power. A comparison with results derived from other theoretical studies shown a quite satisfactory behavior of the present method. The descriptors included in the prediction models permitted the interpretation in structural terms of the permeability process, evidencing the main role of H-bonding and size properties. The models found were used in virtual screening of drug intestinal permeability and a relationship between PCaco-2 calculated and percentage of human intestinal absorption for the 72 compounds was established. These results suggest that the proposed method is able to predict PCaco-2, being a good tool for screening of PCaco-2 for large sets of NCEs synthesized via combinatorial chemistry approach.