Enrichment Assessment of Multiple Virtual Screening Strategies for Toll-Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set

2015 ◽  
Vol 86 (5) ◽  
pp. 1226-1241 ◽  
Author(s):  
Fen Pei ◽  
Hongwei Jin ◽  
Xin Zhou ◽  
Jie Xia ◽  
Lidan Sun ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Toll Like Receptor ◽  
Data Set ◽  
Screening Strategies

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 Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods

2010 ◽  
Vol 50 (12) ◽  
pp. 2079-2093 ◽  
Author(s):  
Vishwesh Venkatraman ◽  
Violeta I. Pérez-Nueno ◽  
Lazaros Mavridis ◽  
David W. Ritchie
Keyword(s):  
Virtual Screening ◽  
Screening Tools ◽  
Data Set ◽  
Comprehensive Comparison ◽  
3D Methods

Extended Virtual Screening Strategies To Link Antiandrogenic Activities and Detected Organic Contaminants in Soils

2017 ◽  
Vol 51 (21) ◽  
pp. 12528-12536 ◽  
Author(s):  
Jing Guo ◽  
Wei Shi ◽  
Qinchang Chen ◽  
Dongyang Deng ◽  
Xiaowei Zhang ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Organic Contaminants ◽  
Screening Strategies

scholarly journals Toll-like receptor 2 antagonists identified through virtual screening and experimental validation

FEBS Journal ◽  
2017 ◽  
Vol 284 (14) ◽  
pp. 2264-2283 ◽  
Author(s):  
Prasannavenkatesh Durai ◽  
Hyeon-Jun Shin ◽  
Asma Achek ◽  
Hyuk-Kwon Kwon ◽  
Rajiv Gandhi Govindaraj ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Experimental Validation ◽  
Toll Like Receptor ◽  
Toll Like Receptor 2

scholarly journals PSOVina: The hybrid particle swarm optimization algorithm for protein–ligand docking

2015 ◽  
Vol 13 (03) ◽  
pp. 1541007 ◽  
Author(s):  
Marcus C. K. Ng ◽  
Simon Fong ◽  
Shirley W. I. Siu
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Screening ◽  
Particle Swarm ◽  
Ligand Docking ◽  
Conformational Search ◽  
Search Problem ◽  
Data Set ◽  
Swarm Optimization ◽  
Screening Experiments ◽  
Hybrid Particle Swarm Optimization

Protein–ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden–Fletcher–Goldfarb–Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein–ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51–60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein–ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

Toward a Benchmarking Data Set Able to Evaluate Ligand- and Structure-based Virtual Screening Using Public HTS Data

2015 ◽  
Vol 55 (2) ◽  
pp. 343-353 ◽  
Author(s):  
Martin Lindh ◽  
Fredrik Svensson ◽  
Wesley Schaal ◽  
Jin Zhang ◽  
Christian Sköld ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Data Set

Novel toll-like receptor 4 (TLR4) antagonists identified by structure- and ligand-based virtual screening

2013 ◽  
Vol 70 ◽  
pp. 393-399 ◽  
Author(s):  
Urban Švajger ◽  
Boris Brus ◽  
Samo Turk ◽  
Matej Sova ◽  
Vesna Hodnik ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Toll Like Receptor ◽  
Toll Like Receptor 4
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