Literature DB >> 29989806

Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark.

Thomas Gaillard1.   

Abstract

Computer-aided protein-ligand binding predictions are a valuable help in drug discovery. Protein-ligand docking programs generally consist of two main components: a scoring function and a search algorithm. It is of interest to evaluate the intrinsic performance of scoring functions, independently of conformational exploration, to understand their strengths and weaknesses and suggest improvements. The comparative assessment of scoring functions (CASF) provides such an evaluation. Here we add the AutoDock and Vina scoring functions to the CASF-2013 benchmark. We find that these popular, free software docking programs are generally in the first half (AutoDock) and first quarter (Vina) among all methods tested in CASF-2013. Vina is the best of all methods in terms of docking power. We also find that ligand minimization has an important impact, reducing the performance difference between AutoDock and Vina.

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Year:  2018        PMID: 29989806     DOI: 10.1021/acs.jcim.8b00312

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  53 in total

1.  Incorporating Explicit Water Molecules and Ligand Conformation Stability in Machine-Learning Scoring Functions.

Authors:  Jianing Lu; Xuben Hou; Cheng Wang; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2019-10-31       Impact factor: 4.956

2.  Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking.

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Journal:  J Chem Inf Model       Date:  2021-09-01       Impact factor: 6.162

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Review 4.  Delta Machine Learning to Improve Scoring-Ranking-Screening Performances of Protein-Ligand Scoring Functions.

Authors:  Chao Yang; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2022-05-17       Impact factor: 6.162

5.  Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations.

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Journal:  Front Microbiol       Date:  2022-05-09       Impact factor: 6.064

7.  Accelerated CDOCKER with GPUs, Parallel Simulated Annealing, and Fast Fourier Transforms.

Authors:  Xinqiang Ding; Yujin Wu; Yanming Wang; Jonah Z Vilseck; Charles L Brooks
Journal:  J Chem Theory Comput       Date:  2020-05-18       Impact factor: 6.006

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Authors:  Karunakaran Kalesh; Sandeep Sundriyal; Hirunika Perera; Steven L Cobb; Paul W Denny
Journal:  mSystems       Date:  2021-05-11       Impact factor: 6.496

9.  In Silico Analysis of the L-2-Hydroxyglutarate Dehydrogenase Gene Mutations and Their Biological Impact on Disease Etiology.

Authors:  Muhammad Muzammal; Alessandro Di Cerbo; Eman M Almusalami; Arshad Farid; Muzammil Ahmad Khan; Shakira Ghazanfar; Mohammed Al Mohaini; Abdulkhaliq J Alsalman; Yousef N Alhashem; Maitham A Al Hawaj; Abdulmonem A Alsaleh
Journal:  Genes (Basel)       Date:  2022-04-15       Impact factor: 4.141

10.  Exploring the Mechanism of Scutellaria baicalensis Georgi Efficacy against Oral Squamous Cell Carcinoma Based on Network Pharmacology and Molecular Docking Analysis.

Authors:  Fanfan Hou; Yang Liu; YaHsin Cheng; Ni Zhang; Wenpeng Yan; Fang Zhang
Journal:  Evid Based Complement Alternat Med       Date:  2021-07-13       Impact factor: 2.629

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