scholarly journals Computation of Vertex-Based Topological Descriptors of Organometallic Monolayers of TM 3 C 12 S 12

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dalal Alrowaili ◽  
Faraha Ashraf ◽  
Rifaqat Ali ◽  
Arsalan Shoukat ◽  
Aqila Shaheen ◽  
...  
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Topological Descriptors ◽  
Quantitative Structure ◽  
Structure Property ◽  
Biological Structure ◽  
Qsar Modeling ◽  
Chemical Structures ◽  
Structure Activity ◽  
Structure Property Relationship ◽  
Mathematical Relationships

Topological descriptors are mathematical values related to chemical structures which are associated with different physicochemical properties. The use of topological descriptors has a great contribution in the field of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) modeling. These are mathematical relationships between different molecular properties or biological activity and some other physicochemical or structural properties. In this article, we calculate few vertex degree-based topological indices/descriptors of the organometallic monolayer structure. At present, the numerical programming of the biological structure with topological descriptors is increasing in consequence in invigorating science, bioinformatics, and pharmaceutics.

scholarly journals Topological indices of rhombus type silicate and oxide networks

2017 ◽  
Vol 95 (2) ◽  
pp. 134-143 ◽  
Author(s):  
M. Javaid ◽  
Masood Ur Rehman ◽  
Jinde Cao
Keyword(s):  
Molecular Graph ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Topological Indices ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Activity ◽  
Structure Property Relationship ◽  
Cartesian Coordinate ◽  
Atom Bond

For a molecular graph, a numeric quantity that characterizes the whole structure of a graph is called a topological index. In the studies of quantitative structure – activity relationship (QSAR) and quantitative structure – property relationship (QSPR), topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we compute general Randić, first general Zagreb, generalized Zagreb, multiplicative Zagreb, atom-bond connectivity (ABC), and geometric arithmetic (GA) indices for the rhombus silicate and rhombus oxide networks. In addition, we also compute the latest developed topological indices such as the fourth version of ABC (ABC4), the fifth version of GA (GA5), augmented Zagreb, and Sanskruti indices for the foresaid networks. At the end, a comparison between all the indices is included, and the result is shown with the help of a Cartesian coordinate system.

scholarly journals The General Connectivity and General Sum-Connectivity Indices of Nanostructures

2015 ◽  
Vol 44 ◽  
pp. 73-80
Author(s):  
Mohammad Reza Farahani
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Connectivity Index ◽  
Quantitative Structure ◽  
Structure Property ◽  
The Third ◽  
Structure Activity ◽  
New Variant ◽  
Connectivity Indices ◽  
Structure Property Relationship ◽  
Vertex Set

Let G be a simple graph with vertex set V(G) and edge set E(G). For ∀νi∈V(G),di denotes the degree of νi in G. The Randić connectivity index of the graph G is defined as [1-3] χ(G)=∑e=v1v2є(G)(d1d2)-1/2. The sum-connectivity index is defined as χ(G)=∑e=v1v2є(G)(d1+d2)-1/2. The sum-connectivity index is a new variant of the famous Randić connectivity index usable in quantitative structure-property relationship and quantitative structure-activity relationship studies. The general m-connectivety and general m-sum connectivity indices of G are defined as mχ(G)=∑e=v1v2...vim+1(1/√(di1di2...dim+1)) and mχ(G)=∑e=v1v2...vim+1(1/√(di1+di2+...+dim+1)) where vi1vi2...vim+1 runs over all paths of length m in G. In this paper, we introduce a closed formula of the third-connectivity index and third-sum-connectivity index of nanostructure "Armchair Polyhex Nanotubes TUAC6[m,n]" (m,n≥1).

scholarly journals Bounds for the Topological Indices of ℘ graph

2021 ◽  
Vol 14 (2) ◽  
pp. 340-350
Author(s):  
Muddalapuram Manjunath ◽  
V. Lokesha ◽  
. Suvarna ◽  
Sushmitha Jain
Keyword(s):  
Finite Graph ◽  
Quantitative Structure Activity Relationship ◽  
Topological Indices ◽  
Quantitative Structure ◽  
Structure Property ◽  
Lower And Upper Bounds ◽  
Zagreb Index ◽  
Chemical Structures ◽  
Structure Property Relationship ◽  
Atom Bond

Topological indices are mathematical measure which correlates to the chemical structures of any simple finite graph. These are used for Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR). In this paper, we define operator graph namely, ℘ graph and structured properties. Also, establish the lower and upper bounds for few topological indices namely, Inverse sum indeg index, Geometric-Arithmetic index, Atom-bond connectivity index, first zagreb index and first reformulated Zagreb index of ℘-graph.

scholarly journals On Degree Based Topological Indices of TiO2 Crystal via M-Polynomial

2022 ◽  
Vol 19 (2) ◽  
pp. 2022
Author(s):  
Tapan Kumar Baishya ◽  
Bijit Bora ◽  
Pawan Chetri ◽  
Upashana Gogoi
Keyword(s):  
Molecular Graph ◽  
Quantitative Structure Activity Relationship ◽  
Topological Indices ◽  
Three Dimensions ◽  
Topological Descriptors ◽  
Quantitative Structure ◽  
Structure Property ◽  
Physiochemical Properties ◽  
3 Dimensional ◽  
Structure Property Relationship

Topological indices (TI) (descriptors) of a molecular graph are very much useful to study various physiochemical properties. It is also used to develop the quantitative structure-activity relationship (QSAR), quantitative structure-property relationship (QSPR) of the corresponding chemical compound. Various techniques have been developed to calculate the TI of a graph. Recently a technique of calculating degree-based TI from M-polynomial has been introduced. We have evaluated various topological descriptors for 3-dimensional TiO2 crystals using M-polynomial. These descriptors are constructed such that it contains 3 variables (m, n and t) each corresponding to a particular direction. These 3 variables facilitate us to deeply understand the growth of TiO2 in 1 dimension (1D), 2 dimensions (2D), and 3 dimensions (3D) respectively. HIGHLIGHTS Calculated degree based Topological indices of a 3D crystal from M-polynomial A relation among various Topological indices is established geometrically Variations of Topological Indices along three dimensions (directions) are shown geometrically Harmonic index approximates the degree variation of oxygen atom

scholarly journals Minimum Detour Index of Tricyclic Graphs

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Wei Fang ◽  
Zheng-Qun Cai ◽  
Xiao-Xin Li
Keyword(s):  
Connected Graph ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Structure Activity ◽  
Structure Property Relationship ◽  
Longest Paths ◽  
Tricyclic Graphs

The detour index of a connected graph is defined as the sum of the detour distances (lengths of longest paths) between unordered pairs of vertices of the graph. The detour index is used in various quantitative structure-property relationship and quantitative structure-activity relationship studies. In this paper, we characterize the minimum detour index among all tricyclic graphs, which attain the bounds.

scholarly journals CPE-DB: An Open Database of Chemical Penetration Enhancers

Pharmaceutics ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 66
Author(s):  
Ekaterina P. Vasyuchenko ◽  
Philipp S. Orekhov ◽  
Grigoriy A. Armeev ◽  
Marine E. Bozdaganyan
Keyword(s):  
Similarity Search ◽  
Molecular Diversity ◽  
Chemical Nature ◽  
Quantitative Structure Activity Relationship ◽  
Penetration Enhancers ◽  
Quantitative Structure ◽  
Structure Property ◽  
Structure Activity ◽  
Structure Property Relationship ◽  
Chemical Penetration Enhancers

The cutaneous delivery route currently accounts for almost 10% of all administered drugs and it is becoming more common. Chemical penetration enhancers (CPEs) increase the transport of drugs across skin layers by different mechanisms that depend on the chemical nature of the penetration enhancers. In our work, we created a chemical penetration enhancer database (CPE-DB) that is, to the best of our knowledge, the first CPE database. We collected information about known enhancers and their derivatives in a single database, and classified and characterized their molecular diversity in terms of scaffold content, key chemical moieties, molecular descriptors, etc. CPE-DB can be used for virtual screening and similarity search to identify new potent and safe enhancers, building quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) models, and other machine-learning (ML) applications for the prediction of biological activity.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

2018 ◽  
Vol 21 (7) ◽  
pp. 533-542 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei ◽  
Tahereh Momeni Isfahani
Keyword(s):  
Thermodynamic Properties ◽  
Predictive Ability ◽  
Total Sample ◽  
Basis Sets ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Chemical Structures ◽  
Property Relationship ◽  
Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

scholarly journals Prediction of Terpenoid Toxicity Based on a Quantitative Structure–Activity Relationship Model

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 628 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Vibrio Fischeri ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Partial Charge ◽  
Quantitative Structure ◽  
Chemical Structures ◽  
Structure Activity ◽  
Qsar Models ◽  
Toxicity Level

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13), and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites with highly diverse chemical structures. A quantitative structure–activity relationship (QSAR) model to predict terpenoid toxicity and to evaluate the influence of their chemical structures was developed in this study by assessing in real time the toxicity of 27 terpenoid standards using the Gram-negative bioluminescent Vibrio fischeri. Under the test conditions, at a concentration of 1 µM, the terpenoids showed a toxicity level lower than 5%, with the exception of geraniol, citral, (S)-citronellal, geranic acid, (±)-α-terpinyl acetate, and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30% at concentrations of 50–100 µM, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, β-caryophellene, and linalool oxide. Regarding the functional group, terpenoid toxicity was observed in the following order: alcohol > aldehyde ~ ketone > ester > hydrocarbons. The CODESSA software was employed to develop QSAR models based on the correlation of terpenoid toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoid toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., maximum partial charge for a carbon (C) atom (Zefirov’s partial charge (PC)) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with a training coefficient and test coefficient correlation higher than 0.810 and 0.535, respectively, and a square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are suitable and rapid tools to predict terpenoid toxicity in a diversity of food products.

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