References
- Joseph-McCarthy D, Baber JC, Feyfant E, et al. Lead optimization via high-throughput molecular docking. Curr Opin Drug Discov Devel 2007;10:264–74
- Levoin N, Calmels T, Poupardin-Olivier O, et al. Refined docking as a valuable tool for lead optimization: application to histamine h3 receptor antagonists. Arch Pharm Chem Life Sci 2008;341:610–23
- Poli G, Martinelli A, Tuccinardi T. Computational approaches for the identification and optimization of Src family kinases inhibitors. Curr Med Chem 2014;21:3281–93
- Tuccinardi T. Docking-based virtual screening: recent developments. Comb Chem High Throughput Screen 2009;12:303–14
- Kolb P, Ferreira RS, Irwin JJ, Shoichet BK. Docking and chemoinformatic screens for new ligands and targets. Curr Opin Biotechnol 2009;20:429–36
- Ripphausen P, Stumpfe D, Bajorath J. Analysis of structure-based virtual screening studies and characterization of identified active compounds. Future Med Chem 2012;4:603–13
- Wang T, Wu MB, Chen ZJ, et al. Fragment-based drug discovery and molecular docking in drug design. Curr Pharm Biotechnol 2015;16:11–25
- Kumar A, Zhang KY. Hierarchical virtual screening approaches in small molecule drug discovery. Methods 2015;71:26–37
- Singh T, Biswas D, Jayaram B. AADS-an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. J Chem Inf Model 2011;51:2515–27
- Novikov FN, Stroylov VS, Zeifman AA, et al. Lead finder docking and virtual screening evaluation with Astex and DUD test sets. J Comput Aided Mol Des 2012;26:725–35
- Oliva R, Vangone A, Cavallo L. Ranking multiple docking solutions based on the conservation of inter-residue contacts. Proteins 2013;81:1571–84
- Jiang L, Rizzo RC. Pharmacophore-based similarity scoring for DOCK. J Phys Chem B 2015;119:1083–102
- Cao Y, Li L. Improved protein-ligand binding affinity prediction by using a curvature-dependent surface-area model. Bioinformatics 2014;30:1674–80
- Niinivehmas SP, Salokas K, Lätti S, et al. Ultrafast protein structure-based virtual screening with panther. J Comput Aided Mol Des 2015;29:989–1006
- Hoffer L, Chira C, Marcou G, et al. S4MPLE - sampler for multiple protein-ligand entities: methodology and rigid-site docking benchmarking. Molecules 2015;20:8997–9028
- Ding Y, Fang Y, Feinstein WP, et al. GeauxDock: a novel approach for mixed-resolution ligand docking using a descriptor-based force field. J Comput Chem 2015;36:2013–26
- Gaudreault F, Najmanovich RJ. FlexAID: revisiting docking on non-native-complex structures. J Chem Inf Model 2015;55:1323–36
- Segura J, Marín-López MA, Jones PF, et al. VORFFIP-driven dock: V-D2OCK, a fast and accurate protein docking strategy. PLoS One 2015;10:e0118107
- Tanchuk VY, Tanin VO, Vovk AI, Poda G. A new, improved hybrid scoring function for molecular docking and scoring based on autodock and autodock vina. Chem Biol Drug Des 2016;87:618–25
- Yuriev E, Holien J, Ramsland PA. Improvements, trends, and new ideas in molecular docking: 2012-2013 in review. J Mol Recognit 2015;28:581–604
- Dias R, de Azevedo WF, Jr. Molecular docking algorithms. Curr Drug Targets 2008;9:1040–7
- Ferreira LG, Dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules 2015;20:13384–421
- Huang SY, Grinter SZ, Zou X. Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions. Phys Chem Chem Phys 2010;12:12899–908
- Wang JC, Lin JH. Scoring functions for prediction of protein-ligand interactions. Curr Pharm Des 2013;19:2174–82
- Seifert MH. Targeted scoring functions for virtual screening. Drug Discov Today 2009;14:562–9
- Zhong S, Zhang Y, Xiu Z. Rescoring ligand docking poses. Curr Opin Drug Discov Devel 2010;13:326–34
- Balius TE, Mukherjee S, Rizzo RC. Implementation and evaluation of a docking-rescoring method using molecular footprint comparisons. J Comput Chem 2011;32:2273–89
- Lindström A, Edvinsson L, Johansson A, et al. Postprocessing of docked protein-ligand complexes using implicit solvation models. J Chem Inf Model 2011;51:267–82
- Skjærven L, Codutti L, Angelini A, et al. Accounting for conformational variability in protein-ligand docking with NMR-guided rescoring. J Am Chem Soc 2013;135:5819–27
- Da C, Kireev D. Structural protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J Chem Inf Model 2014;54:2555–61
- Lizunov AY, Gonchar AL, Zaitseva NI, Zosimov VV. Accounting for intraligand interactions in flexible ligand docking with a PMF-based scoring function. J Chem Inf Model 2015;55:2121–37
- Charifson PS, Corkery JJ, Murcko MA, Walters WP. Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 1999;42:5100–9
- Feher M. Consensus scoring for protein-ligand interactions. Drug Discov Today 2006;11:421–8
- Teramoto R, Fukunishi H. Supervised consensus scoring for docking and virtual screening. J Chem Inf Model 2007;47:526–34
- Teramoto R, Fukunishi H. Structure-based virtual screening with supervised consensus scoring: evaluation of pose prediction and enrichment factors. J Chem Inf Model 2008;48:747–54
- Avram S, Pacureanu LM, Seclaman E, et al. PLS-DA - docking optimized combined energetic terms (PLSDA-DOCET) protocol: a brief evaluation. J Chem Inf Model 2011;51:3169–79
- Poli G, Tuccinardi T, Rizzolio F, et al. Identification of new Fyn kinase inhibitors using a FLAP-based approach. J Chem Inf Model 2013;53:2538–47
- Park H, Eom JW, Kim YH. Consensus scoring approach to identify the inhibitors of AMP-activated protein kinase α2 with virtual screening. J Chem Inf Model 2014;54:2139–46
- Kelley BP, Brown SP, Warren GL, Muchmore SW. POSIT: flexible shape-guided docking for pose prediction. J Chem Inf Model 2015;55:1771–80
- Gao C, Thorsteinson N, Watson I, et al. Knowledge-based strategy to improve ligand pose prediction accuracy for lead optimization. J Chem Inf Model 2015;55:1460–8
- dos Santos Muniz H, Nascimento AS. Ligand- and receptor-based docking with LiBELa. J Comput Aided Mol Des 2015;29:713–23
- Houston DR, Walkinshaw MD. Consensus docking: improving the reliability of docking in a virtual screening context. J Chem Inf Model 2013;53:384–90
- Tuccinardi T, Poli G, Romboli V, et al. Extensive consensus docking evaluation for ligand pose prediction and virtual screening studies. J Chem Inf Model 2014;54:2980–6
- Poli G, Giuntini N, Martinelli A, Tuccinardi T. Application of a FLAP-consensus docking mixed strategy for the identification of new fatty acid amide hydrolase inhibitors. J Chem Inf Model 2015;55:667–75
- Huang N, Shoichet BK, Irwin JJ. Benchmarking sets for molecular docking. J Med Chem 2006;49:6789–801
- Arciniega M, Lange OF. Improvement of virtual screening results by docking data feature analysis. J Chem Inf Model 2014;54:1401–11
- Tietze S, Apostolakis J. GlamDock: development and validation of a new docking tool on several thousand protein-ligand complexes. J Chem Inf Model 2007;47:1657–72
- Korb O, Stutzle T, Exner TE. Empirical scoring functions for advanced protein-ligand docking with PLANTS. J Chem Inf Model 2009;49:84–96
- Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, et al. rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol 2014;10:e1003571
- Rohrer SG, Baumann K. Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data. J Chem Inf Model 2009;49:169–84
- Sanner MF. Python: a programming language for software integration and development. J Mol Graph Model 1999;17:57–61
- Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comp Chem 2009;30:2785–91
- DOCK, version 6.0. Molecular Design Institute. San Francisco, CA: University of California; 1998
- QUACPAC, version 1.5.0. Santa Fe, NM, USA: OpenEye Scientific Software, Inc.; 2010. Available from: www.eyesopen.com
- FRED, version 3.0.0. Santa Fe, NM, USA: OpenEye Scientific Software, Inc.; 2013. Available from: www.eyesopen.com
- OMEGA, version 2.4.6. Santa Fe, NM, USA: OpenEye Scientific Software; 2013. Available from: www.eyesopen.com
- Hawkins PC, Skillman AG, Warren GL, et al. Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and Cambridge structural database. J Chem Inf Model 2010;50:572–84
- Hawkins PC, Nicholls A. Conformer generation with OMEGA: learning from the data set and the analysis of failures. J Chem Inf Model 2012;52:2919–36
- GLIDE, version 5.0. Portland, OR: Schrödinger Inc.; 2009
- Verdonk ML, Cole JC, Hartshorn MJ, et al. Improved protein-ligand docking using GOLD. Proteins 2013;52:609–23
- Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010;31:455–61
- Korb O, Monecke P, Hessler G, et al. pharmACOphore: multiple flexible ligand alignment based on ant colony optimization. J Chem Inf Model 2010;50:1669–81