DOCK 6: Impact of New Features and Current Docking Performance

By W. J. Allen, Trent E. Balius, Scott R. Brozell, D. Moustakas, P. Therese Lang, David A. Case, Irwin D. Kuntz, Robert C. Rizzo. Journal of Computational Chemistry, 2015

703 citations recorded by the source corpus.

This manuscript presents the latest algorithmic and methodological developments to the structure‐based design program DOCK 6.7 focused on an updated internal energy function, new anchor selection control, enhanced minimization options, a footprint similarity scoring function, a symmetry‐corrected root‐mean‐square deviation algorithm, a database filter, and docking forensic tools. An important strategy during development involved use of three orthogonal metrics for assessment and validation: pose reproduction over a large database of 1043 protein‐ligand complexes (SB2012 test set), cross‐docking to 24 drug‐target protein families, and database enrichment using large active and decoy datasets (Directory of Useful Decoys [DUD]‐E test set) for five important proteins including HIV protease and IGF‐1R. Relative to earlier versions, a key outcome of the work is a significant increase in pose reproduction success in going from DOCK 4.0.2 (51.4%) → 5.4 (65.2%) → 6.7 (73.3%) as a result of significant decreases in failure arising from both sampling 24.1% → 13.6% → 9.1% and scoring 24.4% → 21.1% → 17.5%. Companion cross‐docking and enrichment studies with the new version highlight other strengths and remaining areas for improvement, especially for systems containing metal ions. The source code for DOCK 6.7 is available for download and free for academic users at http://dock.compbio.ucsf.edu/. © 2015 Wiley Periodicals, Inc.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/93e6809c13ded416f08910a726f703bcefd7e36c

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Citations in the corpus

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity (2021, background): “Docking methods [1, 12, 38] play an important role to predict how a specific ligand binds to the target protein with affordable computational costs.”
Key Topics in Molecular Docking for Drug Design (2019, neutral): “Dock6 Anchor-and-grow incremental construction Physics-based (several other options) Academic License Allen, 2015 [42]”
Elucidating modes of activation and herbicide resistance by sequence assembly and molecular modelling of the Acetolactate synthase complex in sugarcane. (2016, methodology): “The final structural model was analyzed in two separate docking experiments using DOCK (v 6.7) (Allen et al., 2015).”
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery (2022, methodology): “These assessments of model performance indicate that certain machine learning-based scoring functions improve prediction accuracy.”
Software for molecular docking: a review (2017, background): “…Glide (Friesner et al. 2004), Cdocker, LigandFit (Venkatachalam et al. 2003), MCDock, FRED (McGann et al. 2003), MOE-Dock (Corbeil et al. 2012), LeDock (Zhao and Caflisch 2013), AutoDock Vina (Trott and Olson 2010), rDock (Ruiz-Carmona et al. 2014), UCSF Dock (Allen et al. 2015), and many others.”
Structure-Based Virtual Screening: From Classical to Artificial Intelligence (2020, methodology): “Then, a VS using DOCK 6 (Allen et al., 2015) with the Amber scoring function was performed.”
Protein structure-based drug design: from docking to molecular dynamics. (2018, methodology): “These include Dock [19], GOLD [20], and AutoDock [21].”
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, result): “7 [43] compared to AUC of 0.852 by AtomNet.”
MedusaDock 2.0: Efficient and Accurate Protein-Ligand Docking With Constraints (2019, background): “Recent advances in the field of computational drug screening have led to major improvements in both scoring and sampling algorithms 6–12 , resulting in increased accuracy and reliability of predictions.”