Using the maximal topological distance matrix for QSPR modeling of the boiling points of cyclic hydrocarbons

1999 ◽  
Vol 40 (1) ◽  
pp. 169-172 ◽  
Author(s):  
A. A. Toropov ◽  
A. P. Toropova ◽  
N. L. Voropaeva ◽  
I. N. Ruban ◽  
S. Sh. Rashidova
Keyword(s):  
Distance Matrix ◽  
Cyclic Hydrocarbons ◽  
Topological Distance ◽  
Boiling Points ◽  
Qspr Modeling

scholarly journals An Improved QSPR Modeling of Hydrocarbon Dipole Moments

2004 ◽  
Vol 4 ◽  
pp. 956-964
Author(s):  
Igor V. Nesterov ◽  
Andrey A. Toropov ◽  
Pablo R. Duchowicz ◽  
Eduardo A. Castro
Keyword(s):  
Regression Analysis ◽  
Dipole Moments ◽  
Distance Matrix ◽  
Basic Number ◽  
Topological Descriptors ◽  
Multivariable Regression Analysis ◽  
Qspr Modeling ◽  
Multivariable Regression ◽  
Electrotopological State

Dipole moments of hydrocarbons are not an easy property to model with conventional 2D descriptors. A comparison of the performance of the most commonly used sets of topological descriptors is presented, each set containing descriptors derived from the regular and Detour distance matrix, Electrotopological State Indices, and the basic number of atoms of each type and bonds. Data were taken on a representative set of 35 hydrocarbon dipole moments previously reported and the classical multivariable regression analysis for establishing the models is employed.

Testing the atomic orbital graph as a basis for QSPR modeling of the boiling points of haloalkanes

1999 ◽  
Vol 40 (6) ◽  
pp. 950-958 ◽  
Author(s):  
A. A. Toropov ◽  
A. P. Toropova ◽  
N. L. Voropaeva ◽  
I. N. Ruban ◽  
S. Sh. Rashidova
Keyword(s):  
Atomic Orbital ◽  
Boiling Points ◽  
Qspr Modeling ◽  
Orbital Graph

scholarly journals QSPR modelling of normal boiling points and octanol/water partition coefficient for acyclic and cyclic hydrocarbons using SMILES-based optimal descriptors

2010 ◽  
Vol 8 (5) ◽  
pp. 1047-1052 ◽  
Author(s):  
A.A. Toropov ◽  
A.P. Toropova ◽  
E. Benfenati
Keyword(s):  
Partition Coefficient ◽  
Structure Property ◽  
Input Line ◽  
Cyclic Hydrocarbons ◽  
Structure Property Relationships ◽  
Boiling Points ◽  
Domain Of Applicability ◽  
Water Partition Coefficient ◽  
Quantitative Structure Property Relationships ◽  
Definition Of

AbstractPredictive quantitative structure - property relationships (QSPR) have been established for normal boiling points and octanol/water partition coefficient for acyclic and cyclic hydrocarbons using optimal descriptors calculated with simplified molecular input line entry system (SMILES). The probabilistic criteria for a rational definition of the domain of applicability of these models are discussed.

scholarly journals New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes

2021 ◽  
pp. 35-35
Author(s):  
Zohreh Faramarzi ◽  
Fatemeh Abbasitabar ◽  
Jalali Jahromi ◽  
Maziar Noei
Keyword(s):  
Statistical Tests ◽  
Model Performance ◽  
Absolute Error ◽  
Qsar Model ◽  
Qspr Model ◽  
Boiling Points ◽  
Azeotropic Mixtures ◽  
Qspr Modeling ◽  
Mathematical Equations ◽  
The Individual

Recently, development of the QSPR models for mixtures has received much attention. The QSPR modeling of mixtures requires the use of appropriate mixture descriptors. In this study, 12 mathematical equations were considered to compute mixture descriptors from the individual components for the prediction of normal boiling points of 78 ternary azeotropic mixtures. Multiple linear regression (MLR) was employed to build all QSPR models. Memorized_ACO algorithm was employed for subset variable selection. An ensemble model was also constructed using averaging strategy to improve the predictability of the final QSAR model. The models have been validated by a test set comprised of 24 ternary azeotropes and by different statistical tests. The resulted ensemble QSPR model had R2training, R2test, and q2 of 0.97, 0.95, and 0.96, respectively. Mean absolute error (MAE) as a good indicator of model performance were found to be 3.06 and 3.52 for training and testing sets, respectively.

Combinatorial Design of Molecule using Activity-Linked Substructural Topological Information as Applied to Antitubercular Compounds

2018 ◽  
Vol 15 (1) ◽  
pp. 67-81 ◽  
Author(s):  
Chandan Raychaudhury ◽  
Md. Imbesat Hassan Rizvi ◽  
Debnath Pal
Keyword(s):  
Biological Activities ◽  
Combinatorial Design ◽  
Combinatorial Structure ◽  
Potential Drug ◽  
Structure Generation ◽  
Topological Information ◽  
Active Compounds ◽  
Antitubercular Drugs ◽  
Topological Distance ◽  
Drug Molecules

Background: Generating a large number of compounds using combinatorial methods increases the possibility of finding novel bioactive compounds. Although some combinatorial structure generation algorithms are available, any method for generating structures from activity-linked substructural topological information is not yet reported. Objective: To develop a method using graph-theoretical techniques for generating structures of antitubercular compounds combinatorially from activity-linked substructural topological information, predict activity and prioritize and screen potential drug candidates. </P><P> Methods: Activity related vertices are identified from datasets composed of both active and inactive or, differently active compounds and structures are generated combinatorially using the topological distance distribution associated with those vertices. Biological activities are predicted using topological distance based vertex indices and a rule based method. Generated structures are prioritized using a newly defined Molecular Priority Score (MPS). Results: Studies considering a series of Acid Alkyl Ester (AAE) compounds and three known antitubercular drugs show that active compounds can be generated from substructural information of other active compounds for all these classes of compounds. Activity predictions show high level of success rate and a number of highly active AAE compounds produced high MPS score indicating that MPS score may help prioritize and screen potential drug molecules. A possible relation of this work with scaffold hopping and inverse Quantitative Structure-Activity Relationship (iQSAR) problem has also been discussed. The proposed method seems to hold promise for discovering novel therapeutic candidates for combating Tuberculosis and may be useful for discovering novel drug molecules for the treatment of other diseases as well.

scholarly journals On the Generalized Distance Energy of Graphs

Mathematics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 17 ◽  
Author(s):  
Abdollah Alhevaz ◽  
Maryam Baghipur ◽  
Hilal A. Ganie ◽  
Yilun Shang
Keyword(s):  
Lower Bounds ◽  
Complete Graph ◽  
Connected Graph ◽  
Distance Matrix ◽  
Wiener Index ◽  
Diagonal Matrix ◽  
Upper And Lower Bounds ◽  
Star Graph ◽  
Extremal Graphs ◽  
Generalized Distance

The generalized distance matrix D α ( G ) of a connected graph G is defined as D α ( G ) = α T r ( G ) + ( 1 − α ) D ( G ) , where 0 ≤ α ≤ 1 , D ( G ) is the distance matrix and T r ( G ) is the diagonal matrix of the node transmissions. In this paper, we extend the concept of energy to the generalized distance matrix and define the generalized distance energy E D α ( G ) . Some new upper and lower bounds for the generalized distance energy E D α ( G ) of G are established based on parameters including the Wiener index W ( G ) and the transmission degrees. Extremal graphs attaining these bounds are identified. It is found that the complete graph has the minimum generalized distance energy among all connected graphs, while the minimum is attained by the star graph among trees of order n.

scholarly journals On the Second-Largest Reciprocal Distance Signless Laplacian Eigenvalue

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 512
Author(s):  
Maryam Baghipur ◽  
Modjtaba Ghorbani ◽  
Hilal A. Ganie ◽  
Yilun Shang
Keyword(s):  
Lower Bounds ◽  
Complete Graph ◽  
Distance Matrix ◽  
Upper And Lower Bounds ◽  
Laplacian Eigenvalue ◽  
Signless Laplacian ◽  
Graph Parameters ◽  
Simple Connected Graph ◽  
Second Largest Eigenvalue ◽  
Reciprocal Distance Matrix

The signless Laplacian reciprocal distance matrix for a simple connected graph G is defined as RQ(G)=diag(RH(G))+RD(G). Here, RD(G) is the Harary matrix (also called reciprocal distance matrix) while diag(RH(G)) represents the diagonal matrix of the total reciprocal distance vertices. In the present work, some upper and lower bounds for the second-largest eigenvalue of the signless Laplacian reciprocal distance matrix of graphs in terms of various graph parameters are investigated. Besides, all graphs attaining these new bounds are characterized. Additionally, it is inferred that among all connected graphs with n vertices, the complete graph Kn and the graph Kn−e obtained from Kn by deleting an edge e have the maximum second-largest signless Laplacian reciprocal distance eigenvalue.

scholarly journals The normalized distance Laplacian

2021 ◽  
Vol 9 (1) ◽  
pp. 1-18
Author(s):  
Carolyn Reinhart
Keyword(s):  
Spectral Radius ◽  
Characteristic Polynomial ◽  
Adjacency Matrix ◽  
Connected Graph ◽  
Distance Matrix ◽  
Laplacian Matrix ◽  
Diagonal Matrix ◽  
The Matrix

Abstract The distance matrix 𝒟(G) of a connected graph G is the matrix containing the pairwise distances between vertices. The transmission of a vertex vi in G is the sum of the distances from vi to all other vertices and T(G) is the diagonal matrix of transmissions of the vertices of the graph. The normalized distance Laplacian, 𝒟𝒧(G) = I−T(G)−1/2 𝒟(G)T(G)−1/2, is introduced. This is analogous to the normalized Laplacian matrix, 𝒧(G) = I − D(G)−1/2 A(G)D(G)−1/2, where D(G) is the diagonal matrix of degrees of the vertices of the graph and A(G) is the adjacency matrix. Bounds on the spectral radius of 𝒟 𝒧 and connections with the normalized Laplacian matrix are presented. Twin vertices are used to determine eigenvalues of the normalized distance Laplacian. The distance generalized characteristic polynomial is defined and its properties established. Finally, 𝒟𝒧-cospectrality and lack thereof are determined for all graphs on 10 and fewer vertices, providing evidence that the normalized distance Laplacian has fewer cospectral pairs than other matrices.

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