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

Virtual Screening and Scaffold Hopping Based on GRID Molecular Interaction Fields

2005 ◽  
Vol 45 (5) ◽  
pp. 1313-1323 ◽  
Author(s):  
Marie M. Ahlström ◽  
Marianne Ridderström ◽  
Kristina Luthman ◽  
Ismael Zamora
Keyword(s):  
Virtual Screening ◽  
Molecular Interaction ◽  
Scaffold Hopping ◽  
Molecular Interaction Fields

High-Throughput Virtual Screening of Proteins Using GRID Molecular Interaction Fields

2009 ◽  
Vol 50 (1) ◽  
pp. 155-169 ◽  
Author(s):  
Simone Sciabola ◽  
Robert V. Stanton ◽  
James E. Mills ◽  
Maria M. Flocco ◽  
Massimo Baroni ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
Molecular Interaction ◽  
Molecular Interaction Fields ◽  
High Throughput Virtual Screening

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 .

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