Quantitative Structure-Activity Relationship (QSAR) Studies for the Inhibition of MAOs

2020 ◽  
Vol 23 (9) ◽  
pp. 887-897
Author(s):  
Muthusamy Ramesh ◽  
Arunachalam Muthuraman
Keyword(s):  
Monoamine Oxidase ◽  
Drug Targets ◽  
Quantitative Structure Activity Relationship ◽  
Monoamine Oxidase Inhibitors ◽  
Oxidative Deamination ◽  
Oxidative Reaction ◽  
Monoamine Oxidases ◽  
Qsar Studies ◽  
Structure Activity ◽  
Production Of Hydrogen

Monoamine oxidases are the crucial drug targets for the treatment of neurodegenerative disorders like depression, Parkinson’s disease, and Alzheimer’s disease. The enzymes catalyze the oxidative deamination of several monoamine containing neurotransmitters, i.e. serotonin (5-HT), melatonin, epinephrine, norepinephrine, phenylethylamine, benzylamine, dopamine, tyramine, etc. The oxidative reaction of monoamine oxidases results in the production of hydrogen peroxide that leads to the neurodegeneration process. Therefore, the inhibition of monoamine oxidases has shown a profound effect against neurodegenerative diseases. At present, the design and development of newer lead molecules for the inhibition of monoamine oxidases are under intensive research in the field of medicinal chemistry. Recently, the advancement in QSAR methodologies has shown considerable interest in the development of monoamine oxidase inhibitors. The present review describes the development of QSAR methodologies, and their role in the design of newer monoamine oxidase inhibitors. It will assist the medicinal chemist in the identification of selective and potent monoamine oxidase inhibitors from various chemical scaffolds.

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.

scholarly journals Multi-task learning with a natural metric for quantitative structure activity relationship learning

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Noureddin Sadawi ◽  
Ivan Olier ◽  
Joaquin Vanschoren ◽  
Jan N. van Rijn ◽  
Jeremy Besnard ◽  
...  
Keyword(s):  
Drug Targets ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Evolutionary Distance ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Task Learning ◽  
Structure Activity ◽  
Feature Based ◽  
Base Learner

Abstract The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of specific compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL significantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for specific drug targets, by leveraging what is known about similar drug targets.

Structure-Activity Relationship of Dicoumarol Derivatives as anti- Staphylococcus aureus (Staph Infection) Agents

2019 ◽  
Vol 17 (2) ◽  
pp. 93-98
Author(s):  
Nidaa Rasheed ◽  
Natalie J. Galant ◽  
Imre G. Csizmadia
Keyword(s):  
Staphylococcus Aureus ◽  
Binding Affinity ◽  
Inverse Correlation ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Qsar Studies ◽  
Structure Activity ◽  
Aromatic Donor ◽  
Direct Binding

<P>Introduction: Staph infection, caused by a bacterium known as Staphylococcus aureus, results in a range of diseases from cellulitis to meningitis. Dicoumarol compounds are now emerging as new anti-Staph infection agents as they possess a different chemical structure than compounds used in previous treatments, in order to combat antibiotic-resistant strains. However, it is unclear how such chemical modulations to the dicoumarol backbone structure achieve higher drug performance. Methods: The following review analyzed various quantitative structure-activity relationship (QSAR) studies on dicoumarol compounds and compared them against the corresponding minimum inhibitory concentration and binding affinity values. Results: Compared to the antimicrobial activity, the dicoumarol derivatives with electron withdrawing substituents, CL, NO2, and CF3 showed an inverse correlation; whereas, the opposite was observed with electron donating compounds such as OH, OMe, and amine groups. Based on the interactions of dicoumarol at the active site, an “aromatic donor-acceptor” relationship was proposed as the method of action for this drug. Furthermore, substituent positioning on the benzene ring was found to exert a greater effect on the binding affinity, speculating that the mechanism of action is two characteristics based, needing, both, the proper aromatic pi-pi interaction for stabilization and direct binding to the OH group in the Tyrosine residue, affected by the steric hindrance. Conclusion: This foundational review can enhance productivity sought by the pharmaceutical agency to use combinational chemistry to increase the efficiency to discover new hits in the synthesis of dicoumarol drugs against Staph infection.</P>

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