scholarly journals Useful Irregularity Indices in QSPR Study for Bismuth Tri-Iodide

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Abaid ur Rehman Virk ◽  
M. A. Rehman ◽  
Ce Shi ◽  
Waqas Nazeer
Keyword(s):  
Chemical Compound ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Mathematical Language ◽  
Quantitative Structure ◽  
Boiling Points ◽  
Structure Activity ◽  
Qspr Study ◽  
Single Number

Topological indices give us a mathematical language to study molecular structures. They convert a chemical compound into a single number which foresees properties, for example, boiling points, viscosity, and the radius of gyrations. Drugs and other chemical compounds are often modeled as various polygonal shapes, trees, and graphs. In this paper, we will compute some irregularity indices for bismuth tri-iodide chain and sheet that are useful in the quantitative structure-activity relationship.

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 Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure–Activity Relationship System

2021 ◽  
Vol 22 (19) ◽  
pp. 10821
Author(s):  
Yasunari Matsuzaka ◽  
Shin Totoki ◽  
Kentaro Handa ◽  
Tetsuyoshi Shiota ◽  
Kota Kurosaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Prediction Models ◽  
Three Dimensional ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Antagonist Activity ◽  
Structure Activity ◽  
Agonist And Antagonist

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure–activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system–which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations—we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.

scholarly journals On Knowledge Discovery and Representations of Molecular Structures Using Topological Indices

2021 ◽  
Vol 11 (1) ◽  
pp. 21-32
Author(s):  
Fawaz E. Alsaadi ◽  
Syed Ahtsham Ul Haq Bokhary ◽  
Aqsa Shah ◽  
Usman Ali ◽  
Jinde Cao ◽  
...  
Keyword(s):  
Topological Index ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Topological Indices ◽  
Molecular Structures ◽  
Quantitative Structure ◽  
Structure Property ◽  
Graph Automorphism ◽  
Structure Property Relationship ◽  
Atom Bond

AbstractThe main purpose of a topological index is to encode a chemical structure by a number. A topological index is a graph invariant, which decribes the topology of the graph and remains constant under a graph automorphism. Topological indices play a wide role in the study of QSAR (quantitative structure-activity relationship) and QSPR (quantitative structure-property relationship). Topological indices are implemented to judge the bioactivity of chemical compounds. In this article, we compute the ABC (atom-bond connectivity); ABC4 (fourth version of ABC), GA (geometric arithmetic) and GA5 (fifth version of GA) indices of some networks sheet. These networks include: octonano window sheet; equilateral triangular tetra sheet; rectangular sheet; and rectangular tetra sheet networks.

Quantitative Structure Activity Relationship of New 7-Oxycoumarin Derivatives as Potent and Selective Monoamine Oxidase a Inhibitors

2013 ◽  
Vol 798-799 ◽  
pp. 1109-1112
Author(s):  
Xian Chao Li ◽  
Hong Zong Si ◽  
Hua Gao ◽  
Hong Lin Zhai ◽  
Yun Bo Duan
Keyword(s):  
Heuristic Method ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Monoamine Oxidase A ◽  
Structural Factors ◽  
Quantitative Structure ◽  
Quantum Chemical Descriptors ◽  
Structure Activity ◽  
Mao A

New series of 4-methyl and 3,4-dimethyl-7-oxycoumarin derivatives showed in vitro high anity and selectivity toward MAO-A isoenzyme. To build the quantitative structure-activity relationships (QSAR) between the molecular structures and the inhibitory of 32 compounds, and to further discuss the structural factors that influenced the selectivity of compounds. The topological, constitutional, geometrical, electrostatic and quantum-chemical descriptors of 32 compounds were calculated by CODESSA, and these descriptors were preselected with the heuristic method (HM). As a result, the four descriptor linear model was developed to describe the relationship between the molecular structures and the selectivity of MAO-A inhibitors. Based on the model, we can also designed new compounds with higher activities finally.

scholarly journals Analysis of SC5C7p,q and NPHXp,q Nanotubes via Topological Indices

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Ye-Jun Ge ◽  
Jia-Bao Liu ◽  
Muhammad Younas ◽  
Muhammad Yousaf ◽  
Waqas Nazeer
Keyword(s):  
Carbon Nanotube ◽  
Closed Form ◽  
Topological Indices ◽  
Molecular Structures ◽  
Quantitative Structure ◽  
Molecular Graphs ◽  
Boiling Points ◽  
Structure Activity ◽  
Electrical Voltage

Scientists are creating materials, for example, a carbon nanotube-based composite created by NASA that bends when a voltage is connected. Applications incorporate the use of an electrical voltage to change the shape (transform) of air ship wings and different structures. Topological indices are numbers related with molecular graphs to allow quantitative structure activity/property/poisonous relationships. Topological indices catch symmetry of molecular structures and give it a scientific dialect to foresee properties, for example, boiling points, viscosity, and the radius of gyrations. We compute M-polynomials of two nanotubes, SC5C7p,q and NPHXp,q. The closed form of M-polynomials for these nanotubes produces formulas of numerous degree-based topological indices which are functions relying on parameters of the structure and, in combination, decide properties of the concerned nanotubes. Moreover, we sketch our results by using Maple 2015 to see the dependence of our results on the involved parameters.

Computational Approaches for the Design of Mosquito Repellent Chemicals

2020 ◽  
Vol 27 (1) ◽  
pp. 32-41 ◽  
Author(s):  
Subhash C. Basak ◽  
Apurba K. Bhattacharjee
Keyword(s):  
Computational Design ◽  
Quantitative Structure Activity Relationship ◽  
Computer Assisted ◽  
Quantitative Structure ◽  
Mosquito Repellent ◽  
Computational Approaches ◽  
Qsar Studies ◽  
Computer Assisted Design ◽  
Structure Activity ◽  
Mosquito Repellents

Background: In view of many current mosquito-borne diseases there is a need for the design of novel repellents. Objective: The objective of this article is to review the results of the researches carried out by the authors in the computer-assisted design of novel mosquito repellents. Methods: Two methods in the computational design of repellents have been discussed: a) Quantitative Structure Activity Relationship (QSAR) studies from a set of repellents structurally related to DEET using computed mathematical descriptors, and b) Pharmacophore based modeling for design and discovery of novel repellent compounds including virtual screening of compound databases and synthesis of novel analogues. Results: Effective QSARs could be developed using mathematical structural descriptors. The pharmacophore based method is an effective tool for the discovery of new repellent molecules. Conclusion: Results reviewed in this article show that both QSAR and pharmacophore based methods can be used to design novel repellent molecules.

Sign in / Sign up

Export Citation Format

Share Document