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

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

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 .

FLAP: GRID Molecular Interaction Fields in Virtual Screening. Validation using the DUD Data Set

2010 ◽  
Vol 50 (8) ◽  
pp. 1442-1450 ◽  
Author(s):  
Simon Cross ◽  
Massimo Baroni ◽  
Emanuele Carosati ◽  
Paolo Benedetti ◽  
Sergio Clementi
Keyword(s):  
Virtual Screening ◽  
Molecular Interaction ◽  
Data Set ◽  
Molecular Interaction Fields

scholarly journals The Integration of Pharmacophore-Based 3D QSAR Modeling and Virtual Screening in Identification of Natural Product Inhibitors against SARS-CoV-2

2020 ◽  
Author(s):  
Samira Norouzi ◽  
Maryam Farahani ◽  
Samad Nejad Ebrahimi
Keyword(s):  
Virtual Screening ◽  
3D Structure ◽  
3D Qsar ◽  
Qsar Model ◽  
Docking Studies ◽  
Molecular Characteristics ◽  
Qsar Modeling ◽  
Data Set ◽  
Public Health Emergencies ◽  
Current Outbreak

Background: The current outbreak of Coronavirus Disease 2019 (SARS-CoV-2) led to public health emergencies all over the world and made it a global concern. Also, the lack of an effective treatment to combat this virus is another concern that has appeared. Today, increasing knowledge of biological structures like increasing computer power brings about a chance to use computational methods efficiently in different phases of the drug discovery and development for helping solve this new global problem. Methods: In this study, 3D pharmacophores were generated based on thirty-one structures with functional affinity inhibition (antiviral drugs used for SARS and MERS) with IC50<250 µM from the literature data. A 3D-QSAR model has been developed and validated to be utilized in virtual screening. Results: The best pharmacophore models have been utilized as 3D queries for virtual screening to gain promising inhibitors from a data set of thousands of natural compounds retrieved from PubChem. The hit compounds were subsequently used for molecular docking studies to investigate their affinity to the 3D structure of the SARS-CoV-2 receptors. The ADMET properties calculate for the hits with high binding affinity. Conclusion: The study outcomes can help understand the molecular characteristics and mechanisms of the binding of hit compounds to SARS-CoV-2 receptors and promising identification inhibitors that are likely to be evolved into drugs.

ChemInform Abstract: FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set.

ChemInform ◽  
2009 ◽  
Vol 40 (15) ◽  
Author(s):  
Timothy J. Cheeseright ◽  
Mark D. Mackey ◽  
James L. Melville ◽  
Jeremy G. Vinter
Keyword(s):  
Virtual Screening ◽  
Data Set ◽  
Molecular Fields

LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening

2020 ◽  
Vol 60 (9) ◽  
pp. 4263-4273 ◽  
Author(s):  
Viet-Khoa Tran-Nguyen ◽  
Célien Jacquemard ◽  
Didier Rognan
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
Data Set

FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set

2008 ◽  
Vol 48 (11) ◽  
pp. 2108-2117 ◽  
Author(s):  
Timothy J. Cheeseright ◽  
Mark D. Mackey ◽  
James L. Melville ◽  
Jeremy G. Vinter
Keyword(s):  
Virtual Screening ◽  
Data Set ◽  
Molecular Fields
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