Quinolone Molecular Structure‐Activity Relationships: What We Have Learned about Improving Antimicrobial Activity

A post-antibiotic world is fast becoming a reality, given the rapid emergence of pathogens that are resistant to current drugs. Therefore, there is an urgent need to discover new classes of potent antimicrobial agents with novel modes of action. Cannabis sativa is an herbaceous plant that has been used for millennia for medicinal and recreational purposes. Its bioactivity is largely due to a class of compounds known as cannabinoids. Recently, these natural products and their analogs have been screened for their antimicrobial properties, in the quest to discover new anti-infective agents. This paper seeks to review the research to date on cannabinoids in this context, including an analysis of structure–activity relationships. It is hoped that it will stimulate further interest in this important issue.

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Generating hypotheses about molecular structure-activity relationships (SARs) by solving an optimization problem

Statistical Analysis and Data Mining The ASA Data Science Journal ◽

10.1002/sam.10040 ◽

2009 ◽

Vol 2 (3) ◽

pp. 161-174

Author(s):

Junshui Ma ◽

Christopher Tong ◽

Andy Liaw ◽

Robert Sheridan ◽

John Szumiloski ◽

...

Keyword(s):

Molecular Structure ◽

Optimization Problem ◽

Structure Activity Relationships ◽

Structure Activity

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Use of Parallel Synthesis To Probe Structure−Activity Relationships among 12-Helical β-Peptides: Evidence of a Limit on Antimicrobial Activity

Journal of the American Chemical Society ◽

10.1021/ja0519785 ◽

2005 ◽

Vol 127 (32) ◽

pp. 11516-11529 ◽

Cited By ~ 67

Author(s):

Emilie A. Porter ◽

Bernard Weisblum ◽

Samuel H. Gellman

Keyword(s):

Antimicrobial Activity ◽

Parallel Synthesis ◽

Structure Activity Relationships ◽

Structure Activity

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Highly functionalized pyranopyrazoles: synthesis, antimicrobial activity, simulation studies and their structure activity relationships (SARs)

Research on Chemical Intermediates ◽

10.1007/s11164-018-3569-8 ◽

2018 ◽

Vol 44 (12) ◽

pp. 7491-7507 ◽

Cited By ~ 4

Author(s):

Guda Mallikarjuna Reddy ◽

Gundala Sravya ◽

Gutha Yuvaraja ◽

Alexandre Camilo ◽

Grigory V. Zyryanov ◽

...

Keyword(s):

Antimicrobial Activity ◽

Simulation Studies ◽

Structure Activity Relationships ◽

Structure Activity

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Structure –Activity Relationships by Autocorrelation Descriptors and Genetic Algorithms

Chemoinformatics and Advanced Machine Learning Perspectives ◽

10.4018/978-1-61520-911-8.ch005 ◽

2011 ◽

pp. 60-94 ◽

Cited By ~ 2

Author(s):

Viviana Consonni ◽

Roberto Todeschini

Keyword(s):

Molecular Structure ◽

Molecular Descriptors ◽

Biological Activities ◽

Chemical Properties ◽

Structural Features ◽

Point Of View ◽

Structure Activity Relationships ◽

Structure Activity ◽

Similarity Relationships ◽

Definition Of

Quantitative Structure-Activity Relationships (QSARs) are models relating variation of molecule properties, such as biological activities, to variation of some structural features of chemical compounds. Three main topics take part of the QSAR/QSPR approach to the scientific research: the representation of molecular structure, the definition of molecular descriptors and the chemoinformatics tools. Molecular descriptors are numerical indices encoding some information related to the molecular structure. They can be both experimental physico-chemical properties of molecules and theoretical indices calculated by mathematical formulas or computational algorithms. In the last few decades, much interest has been addressed to studying how to encompass and convert the information encoded in the molecular structure into one or more numbers used to establish quantitative relationships between structures and properties, biological activities or other experimental properties. Autocorrelation descriptors are a class of molecular descriptors based on the statistical concept of spatial autocorrelation applied to the molecular structure. The objective of this chapter is to investigate the chemical information encompassed by autocorrelation descriptors and elucidate their role in QSAR and drug design. After a short introduction to molecular descriptors from a historical point of view, the chapter will focus on reviewing the different types of autocorrelation descriptors proposed in the literature so far. Then, some methodological topics related to multivariate data analysis will be overviewed paying particular attention to analysis of similarity/diversity of chemical spaces and feature selection for multiple linear regressions. The last part of the chapter will deal with application of autocorrelation descriptors to study similarity relationships of a set of flavonoids and establish QSARs for predicting affinity constants, Ki, to the GABAA benzodiazepine receptor site, BzR.

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Cholesterol: Bioactivities, Structural Modification, Mechanisms of Action, and Structure-Activity Relationships

Mini-Reviews in Medicinal Chemistry ◽

10.2174/1389557521666210105123320 ◽

2021 ◽

Vol 21 ◽

Author(s):

Kong Zhang ◽

Tianze Li ◽

Xijie Shan ◽

Rongfei Lu ◽

Shaoyong Zhang ◽

...

Keyword(s):

Antimicrobial Activity ◽

Antioxidant Activity ◽

Anticancer Activity ◽

Structural Modification ◽

Biological Properties ◽

Mechanisms Of Action ◽

Inflammatory Activity ◽

Anti Psychotic ◽

Structure Activity Relationships ◽

Structure Activity

: Cholesterol, a steroid alcohol, was discovered by M.E. Chevreul in 1815. Cholesterol and its derivatives showed a large variety of biological properties such as anticancer activity, anticardiac activity, anti-inflammatory activity, antimicrobial activity, anti-psychotic activity, antioxidant activity, drug-loaded activity, etc. In this mini-review, the advances of structural modification of cholesterol from 2014 to 2020 were summarized. In addition, the bioactivities, mechanisms of action and structure-activity relationships of cholesterol and its related derivatives were involved.

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