Docking and Virtual Screening in Drug Discovery

2017 ◽  
pp. 255-266 ◽  
Author(s):  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Virtual Screening

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 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

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

scholarly journals Selecting Machine-Learning Scoring Functions for Structure-Based Virtual Screening

2020 ◽  
Author(s):  
Pedro Ballester
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Predictive Accuracy ◽  
Scoring Function ◽  
3D Models ◽  
Large Datasets ◽  
Scoring Functions ◽  
Discovery Process ◽  
Drug Discovery Process

Interest in docking technologies has grown parallel to the ever increasing number and diversity of 3D models for macromolecular therapeutic targets. Structure-Based Virtual Screening (SBVS) aims at leveraging these experimental structures to discover the necessary starting points for the drug discovery process. It is now established that Machine Learning (ML) can strongly enhance the predictive accuracy of scoring functions for SBVS by exploiting large datasets from targets, molecules and their associations. However, with greater choice, the question of which ML-based scoring function is the most suitable for prospective use on a given target has gained importance. Here we analyse two approaches to select an existing scoring function for the target along with a third approach consisting in generating a scoring function tailored to the target. These analyses required discussing the limitations of popular SBVS benchmarks, the alternatives to benchmark scoring functions for SBVS and how to generate them or use them using freely-available software.

scholarly journals Acceleration of TB drug discovery by AI-based virtual screening against underexploited targets

2021 ◽  
Author(s):  
Ha H. Truong ◽  
Sandra Geden ◽  
Rashmi Gupta ◽  
Kyle Rohde
Keyword(s):  
Drug Discovery ◽  
Virtual Screening

scholarly journals A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments

2019 ◽  
Vol 20 (18) ◽  
pp. 4648 ◽  
Author(s):  
Nathalie Lagarde ◽  
Elodie Goldwaser ◽  
Tania Pencheva ◽  
Dessislava Jereva ◽  
Ilza Pajeva ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Web Service ◽  
Molecular Mechanics ◽  
In Silico ◽  
Chemical Biology ◽  
In Silico Screening ◽  
Web Based ◽  
Screening Experiments ◽  
The Web

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

Sign in / Sign up

Export Citation Format

Share Document