A graph theoretical approach to structure-property and structure-activity correlations

1980 ◽  
Vol 58 (1) ◽  
pp. 45-68 ◽  
Author(s):  
Charles L. Wilkins ◽  
Milan Randić
Keyword(s):  
Theoretical Approach ◽  
Structure Property ◽  
Structure Activity ◽  
Graph Theoretical Approach

A Review on Camptothecin Analogs with Promising Cytotoxic Profile

2019 ◽  
Vol 18 (13) ◽  
pp. 1796-1814 ◽  
Author(s):  
Sk. Abdul Amin ◽  
Nilanjan Adhikari ◽  
Tarun Jha ◽  
Shovanlal Gayen
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Structure Property ◽  
Cytotoxic Potential ◽  
Structure Property Relationships ◽  
Structure Activity ◽  
Camptothecin Analogs ◽  
Pharmacokinetic Properties ◽  
Quantitative Structure Activity Relationships

Camptothecin (CPT), obtained from Camptotheca acuminata (Nyssaceae), is a quinoline type of alkaloid. Apart from various traditional uses, it is mainly used as a potential cytotoxic agent acting against a variety of cancer cell lines. Though searches have been continued for last six decades, still it is a demanding task to design potent and cytotoxic CPTs. Different CPT analogs are synthesized to enhance the cytotoxic potential as well as to increase the pharmacokinetic properties of these analogs. Some of these analogs were proven to be clinically effective in different cancer cell lines. In this article, different CPT analogs have been highlighted extensively to get a detail insight about the structure-property relationships as well as different quantitative structure-activity relationships (QSARs) modeling of these analogs are also discussed. This study may be beneficial for designing newer CPT analogs in future.

A Spectral Graph Theoretical Approach to Oriented Energy Features

2017 ◽  
Vol 31 (01) ◽  
pp. 1755001 ◽  
Author(s):  
Güleser Kalaycı Demir
Keyword(s):  
Theoretical Approach ◽  
Graph Laplacian ◽  
Texture Segmentation ◽  
Contour Detection ◽  
Feature Extraction Method ◽  
Spectral Graph ◽  
Graph Theoretical Approach ◽  
Novel Method ◽  
Oriented Energy ◽  
Complex Valued

In this work, we propose a novel method for determining oriented energy features of an image. Oriented energy features, useful for many machine vision applications like contour detection, texture segmentation and motion analysis, are determined from the filters whose outputs are enhanced at the edges of the image at a given orientation. We use the eigenvectors and eigenvalues of graph Laplacian for determining the oriented energy features of an image. Our method is based on spectral graph theoretical approach in which a graph is assigned complex-valued edge weights whose phases encode orientation information. These edge weights give rise to a complex-valued Hermitian Laplacian whose spectrum enables us to extract oriented energy features of the image. We perform a set of numerical experiments to determine the efficiency and characteristics of the proposed method. In addition, we apply our feature extraction method to texture segmentation problem. We do this in comparison with other known methods, and show that our method performs better for various test textures.

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