Amr Alhossary1, Stephanus Daniel Handoko1, Yuguang Mu1, Chee-Keong Kwoh1. 1. 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.
Abstract
MOTIVATION: 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%. AVAILABILITY AND IMPLEMENTATION: The C++ source code of QVina 2 is available at (www.qvina.org). CONTACT: aalhossary@pmail.ntu.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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%. AVAILABILITY AND IMPLEMENTATION: The C++ source code of QVina 2 is available at (www.qvina.org). CONTACT: aalhossary@pmail.ntu.edu.sg SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Lars Elend; Luise Jacobsen; Tim Cofala; Jonas Prellberg; Thomas Teusch; Oliver Kramer; Ilia A Solov'yov Journal: Molecules Date: 2022-06-22 Impact factor: 4.927
Authors: Brian J Bender; Stefan Gahbauer; Andreas Luttens; Jiankun Lyu; Chase M Webb; Reed M Stein; Elissa A Fink; Trent E Balius; Jens Carlsson; John J Irwin; Brian K Shoichet Journal: Nat Protoc Date: 2021-09-24 Impact factor: 17.021
Authors: Patrick D Fischer; Evangelos Papadopoulos; Jon M Dempersmier; Zi-Fu Wang; Radosław P Nowak; Katherine A Donovan; Joann Kalabathula; Christoph Gorgulla; Pierre P M Junghanns; Eihab Kabha; Nikolaos Dimitrakakis; Ognyan I Petrov; Constantine Mitsiades; Christian Ducho; Vladimir Gelev; Eric S Fischer; Gerhard Wagner; Haribabu Arthanari Journal: Eur J Med Chem Date: 2021-04-08 Impact factor: 7.088