AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings
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Diogo Santos-Martins | Stefano Forli | Andreas F. Tillack | Jérôme Eberhardt | A. F. Tillack | Stefano Forli | Diogo Santos-Martins | Jérôme Eberhardt
[1] K. Jarrod Millman,et al. Array programming with NumPy , 2020, Nat..
[2] Shuai Liu,et al. D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions , 2016, Journal of Computer-Aided Molecular Design.
[3] Andreas Koch,et al. D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU , 2019, Journal of Computer-Aided Molecular Design.
[4] Michael M. Mysinger,et al. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking , 2012, Journal of medicinal chemistry.
[5] Cheng Wang,et al. Improving scoring‐docking‐screening powers of protein–ligand scoring functions using random forest , 2017, J. Comput. Chem..
[6] David M. Beazley,et al. SWIG: An Easy to Use Tool for Integrating Scripting Languages with C and C++ , 1996, Tcl/Tk Workshop.
[7] David S. Goodsell,et al. A semiempirical free energy force field with charge‐based desolvation , 2007, J. Comput. Chem..
[8] Lin-Li Li,et al. ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein-Ligand Interactions , 2013, J. Chem. Inf. Model..
[9] Matthew P. Repasky,et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.
[10] et al.,et al. Jupyter Notebooks - a publishing format for reproducible computational workflows , 2016, ELPUB.
[11] Stefano Forli,et al. Covalent docking using autodock: Two‐point attractor and flexible side chain methods , 2016, Protein science : a publication of the Protein Society.
[12] Stefano Forli,et al. AutoDock Bias: improving binding mode prediction and virtual screening using known protein-ligand interactions , 2019, Bioinform..
[13] Thomas Gaillard,et al. Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark , 2018, J. Chem. Inf. Model..
[14] Michael K Gilson,et al. Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. , 2012, The Journal of chemical physics.
[15] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.
[16] T. Lazaridis. Inhomogeneous Fluid Approach to Solvation Thermodynamics. 1. Theory , 1998 .
[17] R. Woods,et al. Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking. , 2016, Journal of chemical theory and computation.
[18] Themis Lazaridis,et al. Inhomogeneous Fluid Approach to Solvation Thermodynamics. 2. Applications to Simple Fluids , 1998 .
[19] Suman Sirimulla,et al. AutoDock VinaXB: implementation of XBSF, new empirical halogen bond scoring function, into AutoDock Vina , 2016, Journal of Cheminformatics.
[20] Song Liu,et al. A knowledge-based energy function for protein-ligand, protein-protein, and protein-DNA complexes. , 2005, Journal of medicinal chemistry.
[21] Ruben Abagyan,et al. Macrocycle modeling in ICM: benchmarking and evaluation in D3R Grand Challenge 4 , 2019, Journal of Computer-Aided Molecular Design.
[22] Minghao Yin,et al. EDock: blind protein–ligand docking by replica-exchange monte carlo simulation , 2020, Journal of Cheminformatics.
[23] Rodrigo Quiroga,et al. Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening , 2016, PloS one.
[24] Yurii S Moroz,et al. ZINC20 - A Free Ultralarge-Scale Chemical Database for Ligand Discovery , 2020, J. Chem. Inf. Model..
[25] Arthur J. Olson,et al. AutoDock4Zn: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins , 2014, J. Chem. Inf. Model..
[26] K. Wüthrich,et al. Directional Phosphorylation and Nuclear Transport of the Splicing Factor SRSF1 Is Regulated by an RNA Recognition Motif. , 2016, Journal of molecular biology.
[27] Guido van Rossum,et al. Python Programming Language , 2007, USENIX Annual Technical Conference.
[28] Stefano Moro,et al. DockBench: An Integrated Informatic Platform Bridging the Gap between the Robust Validation of Docking Protocols and Virtual Screening Simulations , 2015, Molecules.
[29] Douglas R. Houston,et al. Consensus Docking: Improving the Reliability of Docking in a Virtual Screening Context , 2013, J. Chem. Inf. Model..
[30] William J. Allen,et al. DOCK 6: Impact of new features and current docking performance , 2015, J. Comput. Chem..
[31] M. Sanner,et al. Docking flexible cyclic peptides with AutoDock CrankPep. , 2019, Journal of chemical theory and computation.
[32] Leonardo Solis-Vasquez,et al. Accelerating AutoDock4 with GPUs and Gradient-Based Local Search. , 2021, Journal of chemical theory and computation.
[33] Luhua Lai,et al. Further development and validation of empirical scoring functions for structure-based binding affinity prediction , 2002, J. Comput. Aided Mol. Des..
[34] Arthur J. Olson,et al. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading , 2009, J. Comput. Chem..
[35] Ruth Huey,et al. Computational protein–ligand docking and virtual drug screening with the AutoDock suite , 2016, Nature Protocols.
[36] P Willett,et al. Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.
[37] David Ryan Koes,et al. Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise , 2013, J. Chem. Inf. Model..
[38] Shigenori Tanaka,et al. AutoDock-GIST: Incorporating Thermodynamics of Active-Site Water into Scoring Function for Accurate Protein-Ligand Docking , 2016, Molecules.
[39] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.
[40] Thomas Lengauer,et al. A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.
[41] Andreas Koch,et al. Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4 , 2019, Journal of Computer-Aided Molecular Design.
[42] Yaoqi Zhou,et al. DLIGAND2: an improved knowledge-based energy function for protein–ligand interactions using the distance-scaled, finite, ideal-gas reference state , 2019, Journal of Cheminformatics.
[43] Trung Hai Nguyen,et al. Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity , 2019, J. Chem. Inf. Model..
[44] U. Singh,et al. A NEW FORCE FIELD FOR MOLECULAR MECHANICAL SIMULATION OF NUCLEIC ACIDS AND PROTEINS , 1984 .
[45] Sérgio F. Sousa,et al. Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening , 2019, Applied Sciences.
[46] Jian Li,et al. Structural Biology-Inspired Discovery of Novel KRAS-PDEδ Inhibitors. , 2017, Journal of medicinal chemistry.
[47] Liliane Mouawad,et al. Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance , 2016, Journal of Cheminformatics.
[48] Maurizio Botta,et al. Lennard-Jones Potential and Dummy Atom Settings to Overcome the AUTODOCK Limitation in Treating Flexible Ring Systems , 2007, J. Chem. Inf. Model..
[49] Huanwang Yang,et al. D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies , 2020, Journal of Computer-Aided Molecular Design.
[50] Stefano Forli,et al. A force field with discrete displaceable waters and desolvation entropy for hydrated ligand docking. , 2012, Journal of medicinal chemistry.
[51] David S. Goodsell,et al. AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility , 2015, PLoS Comput. Biol..
[52] Chee Keong Kwoh,et al. Fast, accurate, and reliable molecular docking with QuickVina 2 , 2015, Bioinform..
[53] Ajay N. Jain. Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. , 2003, Journal of medicinal chemistry.