Docking and Virtual Screening Strategies for GPCR Drug Discovery

2015 ◽  
pp. 251-276 ◽  
Author(s):  
Thijs Beuming ◽  
Bart Lenselink ◽  
Daniele Pala ◽  
Fiona McRobb ◽  
Matt Repasky ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Screening Strategies

Virtual Screening Strategies in Drug Discovery: A Critical Review

2013 ◽  
Vol 20 (23) ◽  
pp. 2839-2860 ◽  
Author(s):  
A. Lavecchia ◽  
C. Giovanni
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Critical Review ◽  
Screening Strategies

Dose COVID-19 Uncovered a New Feature of Metronidazole Drug?

2020 ◽  
Author(s):  
Mohammad Seyedhamzeh ◽  
Bahareh Farasati Far ◽  
Mehdi Shafiee Ardestani ◽  
Shahrzad Javanshir ◽  
Fatemeh Aliabadi ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Active Sites ◽  
Initial Plasma ◽  
New Feature ◽  
Metronidazole Benzoate ◽  
Global Health Problem ◽  
A Current ◽  
Pro Inflammatory Cytokines

Studies of coronavirus disease 2019 (COVID-19) as a current global health problem shown the initial plasma levels of most pro-inflammatory cytokines increased during the infection, which leads to patient countless complications. Previous studies also demonstrated that the metronidazole (MTZ) administration reduced related cytokines and improved treatment in patients. However, the effect of this drug on cytokines has not been determined. In the present study, the interaction of MTZ with cytokines was investigated using molecular docking as one of the principal methods in drug discovery and design. According to the obtained results, the IL12-metronidazole complex is more stable than other cytokines, and an increase in the surface and volume leads to prevent to bind to receptors. Moreover, ligand-based virtual screening of several libraries showed metronidazole phosphate, metronidazole benzoate, 1-[1-(2-Hydroxyethyl)-5- nitroimidazol-2-yl]-N-methylmethanimine oxide, acyclovir, and tetrahydrobiopterin (THB or BH4) like MTZ by changing the surface and volume prevents binding IL-12 to the receptor. Finally, the inhibition of the active sites of IL-12 occurred by modifying the position of the methyl and hydroxyl functional groups in MTZ. <br>

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Structure-Based Virtual Screening and Biochemical Evaluation for the Identification of Novel Trypanosoma brucei Aldolase Inhibitors

2018 ◽  
Vol 18 (5) ◽  
pp. 397-405 ◽  
Author(s):  
Leonardo L.G. Ferreira ◽  
Rafaela S. Ferreira ◽  
David L. Palomino ◽  
Adriano D. Andricopulo
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Trypanosoma Brucei ◽  
Enzyme Inhibitor ◽  
Practical Knowledge ◽  
Glycolytic Enzyme ◽  
Structure Based Drug Design ◽  
Biochemical Evaluation ◽  
Dynamics Simulations ◽  
Fructose 1,6 Bisphosphate

Introduction: The glycolytic enzyme fructose-1,6-bisphosphate aldolase is a validated molecular target in human African trypanosomiasis (HAT) drug discovery, a neglected tropical disease (NTD) caused by the protozoan Trypanosoma brucei. Herein, a structure-based virtual screening (SBVS) approach to the identification of novel T. brucei aldolase inhibitors is described. Distinct molecular docking algorithms were used to screen more than 500,000 compounds against the X-ray structure of the enzyme. This SBVS strategy led to the selection of a series of molecules which were evaluated for their activity on recombinant T. brucei aldolase. The effort led to the discovery of structurally new ligands able to inhibit the catalytic activity of the enzyme. Results: The predicted binding conformations were additionally investigated in molecular dynamics simulations, which provided useful insights into the enzyme-inhibitor intermolecular interactions. Conclusion: The molecular modeling results along with the enzyme inhibition data generated practical knowledge to be explored in further structure-based drug design efforts in HAT drug discovery.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Design, Synthesis and In-Vitro Biological Activity of Some New 1,3-Thiazolidine-4-One Derivatives as Chemotherapeutic Agents Using Virtual Screening Strategies

2020 ◽  
Vol 16 ◽  
Author(s):  
Pragya Nayak ◽  
Monica Kachroo
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Virtual Screening ◽  
In Silico ◽  
Cancer Cell Line ◽  
Inhibitory Response ◽  
Chemotherapeutic Agents ◽  
Acceptor Site ◽  
Hydrogen Bond Acceptor ◽  
Screening Strategies

: A series of new heteroaryl thiazolidine-4-one derivatives were designed and subjected to in-silico prioritization using various virtual screening strategies. Two series of thiazolidinone derivatives were synthesized and screened for their in-vitro antitubercular, anticancer, antileishmanial and antibacterial (Staphylococcus aureus; Streptococcus pneumonia; Escherichia coli; Pseudomonas aeruginosa) activities. The compounds with electronegative substitutions exhibited positive antitubercular activity, the derivatives possessing a methyl substitution exhibited good inhibitory response against breast cancer cell line MCF-7 while the compounds possessing a hydrogen bond acceptor site like hydroxyl and methoxy substitution in their structures exhibited good in-vitro antileishmanial activity. Some compounds exhibited potent activity against gram positive bacteria Pseudomonas aeruginosa as compared to the standards. Altogether, the designed compounds exhibited good in-vitro anti-infective potential which was in good agreement with the in-silico predictions and they can be developed as important lead molecules for anti-infective and chemotherapeutic drug research.

scholarly journals Structure-based high throughput virtual screening for drug discovery

2002 ◽  
Vol 58 (s1) ◽  
pp. c67-c67
Author(s):  
H. Jiang ◽  
J. Shen ◽  
X. Luo ◽  
H. Liu ◽  
F. Chen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Virtual Screening
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