title: Fast, accurate, and reliable molecular docking with QuickVina 2.
abstract: BACKGROUND
  The need for efficient molecular docking tools for high-throughput screening is growing alongside the rapid growth of drug-fragment databases. AutoDock Vina ('Vina') is a widely used docking tool with parallelization for speed. QuickVina ('QVina 1') then further enhanced the speed via a heuristics, requiring high exhaustiveness. With low exhaustiveness, its accuracy was compromised. We present in this article the latest version of QuickVina ('QVina 2') that inherits both the speed of QVina 1 and the reliability of the original Vina.
  
  
  RESULTS
  We tested the efficacy of QVina 2 on the core set of PDBbind 2014. With the default exhaustiveness level of Vina (i.e. 8), a maximum of 20.49-fold and an average of 2.30-fold acceleration with a correlation coefficient of 0.967 for the first mode and 0.911 for the sum of all modes were attained over the original Vina. A tendency for higher acceleration with increased number of rotatable bonds as the design variables was observed. On the accuracy, Vina wins over QVina 2 on 30% of the data with average energy difference of only 0.58 kcal/mol. On the same dataset, GOLD produced RMSD smaller than 2 Å on 56.9% of the data while QVina 2 attained 63.1%.
  
  
  METHODS
  The C++ source code of QVina 2 is available at (www.qvina.org).
  
  
  BACKGROUND
  aalhossary@pmail.ntu.edu.sg
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.
authors.forename: Amr
authors.initials: A
authors.lastname: Alhossary
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.
authors.forename: Stephanus Daniel
authors.initials: SD
authors.lastname: Handoko
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.
authors.forename: Yuguang
authors.initials: Y
authors.lastname: Mu
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore 639798, School of Information Systems, Singapore Management University, Singapore 188065, and School of Biological Sciences, Nanyang Technological University, Singapore 637551.
authors.forename: Chee-Keong
authors.initials: CK
authors.lastname: Kwoh
mesh_terms: D019295:Computational Biology / Q000379:methods*; D062313:Databases, Pharmaceutical; D015195:Drug Design; D006801:Humans; D008024:Ligands; D062105:Molecular Docking Simulation / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software
chemical_list: D008024:Ligands; D011506:Proteins