Supercomputer Docking
DOI:
https://doi.org/10.14529/jsfi190302Abstract
This review is based on the peer–reviewed research literature including the author’s own publications devoted to supercomputer docking. The general view on docking and its role at the initial stage of the rational drug design is presented. Molecules of medicine compounds selectively bind to the active site of a protein, which is responsible for the disease progression, and stop it. Docking programs perform positioning of molecules (ligands) in the active site of the protein and estimate the protein–ligand binding energy. The larger this energy is, the less concentration of the respective compound should be used to observe the desired effect. Several classical docking programs are described in short. Examples of the adaptation of existing docking programs to supercomputing and using them for virtual screening of millions of ligands are presented. Two novel generalized docking programs specially designed for multi–core docking of a single ligand on a supercomputer are described shortly. These programs find a sufficiently wide spectrum of low energy minima of a protein–ligand complex in the frame of a given force field. The quasi–docking procedure using the generalized docking program is described. Quasi–docking allows to perform docking with quantum–chemical semiempirical methods. Finally a summary is made based on the materials presented.
References
Allen, W.J., Balius, T.E., Mukherjee, S., et al.: Dock 6: Impact of new features and current docking performance. Journal of Computational Chemistry 36(15), 1132–56 (2015), DOI: 10.1002/jcc.23905
Arcon, J.P., Defelipe, L.A., Modenutti, C.P., et al.: Molecular dynamics in mixed solvents reveals proteinligand interactions, improves docking, and allows accurate binding free energy predictions. Journal of Chemical Information and Modeling 57(4), 846–863 (2017), DOI: 10.1021/acs.jcim.6b00678
Basciu, A., Malloci, G., Pietrucci, F., et al.: Holo–like and druggable protein conformations from enhanced sampling of binding pocket volume and shape. Journal of Chemical Information and Modeling 59(4), 1515–1528 (2019), DOI: 10.1021/acs.jcim.8b00730
Beierlein, F., Lanig, H., Schrer, G., et al.: Quantum mechanical/molecular mechanical (QM/MM) docking: An evaluation for known test systems. Molecular Physics – MOL PHYS 101, 2469–2480 (2003), DOI: 10.1080/0026897031000092940
Bekker, G.J., Araki, M., Oshima, K., et al.: Dynamic docking of a medium–sized molecule to its receptor by multicanonical MD simulations. The Journal of Physical Chemistry B 123(11), 2479–2490 (2019), DOI: 10.1021/acs.jpcb.8b12419
Berman, H.M., Westbrook, J., Feng, Z., et al.: The protein data bank. Nucleic Acids Research 28(1), 235–42 (2000), DOI: 10.1093/nar/28.1.235
Best, R.B., Zhu, X., Shim, J., et al.: Optimization of the additive CHARMM all–atom protein force field targeting improved sampling of the backbone phi, psi and side–chain chi(1) and chi(2) dihedral angles. Journal of Chemical Theory and Computation 8(9), 3257–3273 (2012), DOI: 10.1021/ct300400x
Bikadi, Z., Hazai, E.: Application of the PM6 semi–empirical method to modeling proteins enhances docking accuracy of AutoDock. Journal of cheminformatics 1, 15–15 (2009), DOI: 10.1186/1758-2946-1-15
Bolcato, G., Cuzzolin, A., Bissaro, M., et al.: Can we still trust docking results? An extension of the applicability of DockBench on PDBbind database. International journal of molecular sciences 20(14), 3558 (2019), DOI: 10.3390/ijms20143558
ten Brink, T., Exner, T.E.: Influence of protonation, tautomeric, and stereoisomeric states on protein–ligand docking results. Journal of Chemical Information and Modeling 49(6), 1535–1546 (2009), DOI: 10.1021/ci800420z
ten Brink, T., Exner, T.E.: pka based protonation states and microspecies for proteinligand docking. Journal of Computer–Aided Molecular Design 24(11), 935–942 (2010), DOI: 10.1007/s10822-010-9385-x
Brozell, S.R., Mukherjee, S., Balius, T.E., et al.: Evaluation of dock 6 as a pose generation and database enrichment tool. Journal of Computer–Aided Molecular Design 26(6), 749–773 (2012), DOI: 10.1007/s10822-012-9565-y
Burley, S.K., Berman, H.M., Bhikadiya, C., et al.: RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Research 47(D1), D464–D474 (2018), DOI: 10.1093/nar/gky1004
Chen, Y.C.: Beware of docking! Trends in Pharmacological Sciences 36(2), 78–95 (2015), DOI: 10.1016/j.tips.2014.12.001
Chung, J.Y., Hah, J.M., Cho, A.E.: Correlation between performance of QM/MM docking and simple classification of binding sites. Journal of Chemical Information and Modeling 49(10), 2382–2387 (2009), DOI: 10.1021/ci900231p
Cole, D.J., Tirado-Rives, J., Jorgensen, W.L.: Molecular dynamics and monte carlo simulations for protein–ligand binding and inhibitor design. Biochimica et biophysica acta 1850(5), 966–971 (2015), DOI: 10.1016/j.bbagen.2014.08.018
Collignon, B., Schulz, R., Smith, J.C., et al.: Task–parallel message passing interface implementation of Autodock4 for docking of very large databases of compounds using high–performance super–computers. Journal of Computational Chemistry 32(6), 1202–9 (2011), DOI: 10.1002/jcc.21696
De Vivo, M., Cavalli, A.: Recent advances in dynamic docking for drug discovery. Wiley Interdisciplinary Reviews: Computational Molecular Science 7(6), e1320 (2017), DOI: 10.1002/wcms.1320
Dixit, S.B., Chipot, C.: Can absolute free energies of association be estimated from molecular mechanical simulations? The biotin–streptavidin system revisited. The Journal of Physical Chemistry A 105(42), 9795–9799 (2001), DOI: 10.1021/jp011878v
Dolezal, R., Nepovimova, E., Melikova, M., et al.: Structure–based virtual screening for novel modulators of human orexin 2 receptor with cloud systems and supercomputers. In: Advanced Topics in Intelligent Information and Database Systems, pp. 161–171. Springer International Publishing (2017), DOI: 10.1007/978-3-319-56660-3_15
Ellingson, S.R., Dakshanamurthy, S., Brown, M., et al.: Accelerating Virtual High–Throughput Ligand Docking: current technology and case study on a petascale supercomputer. Concurrency and computation: practice & experience 26(6), 1268–1277 (2014), DOI: 10.1002/cpe.3070
Elokely, K.M., Doerksen, R.J.: Docking challenge: protein sampling and molecular docking performance. Journal of chemical information and modeling 53(8), 1934–1945 (2013), DOI: 10.1021/ci400040d
Evangelista Falcon, W., Ellingson, S.R., Smith, J.C., et al.: Ensemble docking in drug discovery: How many protein configurations from molecular dynamics simulations are needed to reproduce known ligand binding? The Journal of Physical Chemistry B 123(25), 5189–5195 (2019), DOI: 10.1021/acs.jpcb.8b11491
Feixas, F., Lindert, S., Sinko, W., et al.: Exploring the role of receptor flexibility in structure–based drug discovery. Biophysical chemistry 186, 31–45 (2014), DOI: 10.1016/j.bpc.2013.10.007
Filamofitsky, M.P.: The system X–Com for metacomputing support: architecture and technology. Numerical Methods and Programming 5(2), 1–9 (in Russian) (2004)
Forli, S., Huey, R., Pique, M.E., et al.: Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nature protocols 11(5), 905–19 (2016), DOI: 10.1038/nprot.2016.051
Friesner, R.A., Murphy, R.B., Repasky, M.P., et al.: Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein–ligand complexes. Journal of Medicinal Chemistry 49(21), 6177–96 (2006), DOI: 10.1021/jm051256o
Genheden, S., Nilsson, I., Ryde, U.: Binding affinities of factor Xa inhibitors estimated by thermodynamic integration and MM/GBSA. Journal of Chemical Information and Modeling 51(4), 947–958 (2011), DOI: 10.1021/ci100458f
Gibbs, N., Clarke, A.R., Sessions, R.B.: Ab initio protein structure prediction using physicochemical potentials and a simplified off–lattice model. Proteins: Structure, Function, and Bioinformatics 43(2), 186–202 (2001), DOI: 10.1002/1097-0134(20010501)43:2%3C186::aidprot1030%3E3.0.co;2-l
Gioia, D., Bertazzo, M., Recanatini, M., et al.: Dynamic docking: A paradigm shift in computational drug discovery. Molecules (Basel, Switzerland) 22(11), 2029 (2017), DOI: 10.3390/molecules22112029
Goreinov, S., Tyrtyshnikov, E.: The maximal–volume concept in approximation by low–rank matrices. Contemporary Mathematics 268, 47–51 (2001)
Halgren, T.A.: Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. Journal of Computational Chemistry 17(5-6), 490–519 (1996), DOI: 10.1002/(SICI)1096-987X(199604)17:5/6%3C490::AID-JCC1%3E3.0.CO;2-P
Honegr, J., Dolezal, R., Malinak, D., et al.: Rational design of a new class of toll–like receptor 4 (TLR4) tryptamine related agonists by means of the structure– and ligand–based virtual screening for vaccine adjuvant discovery. Molecules 23(1) 102 (2018), DOI: 10.3390/molecules23010102
Hostas, J., Rezac, J., Hobza, P.: On the performance of the semi-empirical quantum mechanical PM6 and PM7 methods for noncovalent interactions. Chemical Physics Letters 568-569(Supplement C), 161–166 (2013), DOI: 10.1016/j.cplett.2013.02.069
Huang, N., Shoichet, B.K., Irwin, J.J.: Benchmarking sets for molecular docking. Journal of Medicinal Chemistry 49(23), 6789–6801 (2006), DOI: 10.1021/jm0608356
Irwin, J.J., Sterling, T., Mysinger, M.M., et al.: ZINC: A free tool to discover chemistry for biology. Journal of Chemical Information and Modeling 52(7), 1757–1768 (2012), DOI: 10.1021/ci3001277
Jacq, N., Breton, V., Chen, H.Y., et al.: Virtual screening on large scale grids. Parallel Computing 33(4), 289–301 (2007), DOI: 10.1016/j.parco.2007.02.010
Jaghoori, M.M., Bleijlevens, B., Olabarriaga, S.D.: 1001 Ways to run AutoDock Vina for virtual screening. Journal of Computer–Aided Molecular Design 30(3), 237–249 (2016), DOI: 10.1007/s10822-016-9900-9
Jorgensen, W.L., Tirado-Rives, J.: Potential energy functions for atomic–level simulations of water and organic and biomolecular systems. Proceedings of the National Academy of Sciences of the United States of America 102(19), 6665–70 (2005), DOI: 10.1073/pnas.0408037102
Kalliokoski, T., Salo, H.S., Lahtela-Kakkonen, M., et al.: The effect of ligand–based tautomer and protomer prediction on structure–based virtual screening. Journal of Chemical Information and Modeling 49(12), 2742–2748 (2009), DOI: 10.1021/ci900364w
Kantardjiev, A.A.: Quantum.ligand.dock: protein–ligand docking with quantum entanglement refinement on a GPU system. Nucleic acids research 40(Web Server issue), W415–W422 (2012), DOI: 10.1093/nar/gks515
Khavrutskii, I.V., Wallqvist, A.: Improved binding free energy predictions from single–reference thermodynamic integration augmented with hamiltonian replica exchange. Journal of Chemical Theory and Computation 7(9), 3001–3011 (2011), DOI: 10.1021/ct2003786
Klamt, A., Schuurmann, G.: COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. Journal of the Chemical Society, Perkin Transactions 2 (5), 799–805 (1993), DOI: 10.1039/P29930000799
Klebe, G.: The use of thermodynamic and kinetic data in drug discovery: Decisive insight or increasing the puzzlement? ChemMedChem 10(2), 229–231 (2015), DOI: 10.1002/cmdc.201402521
Klimovich, P.V., Shirts, M.R., Mobley, D.L.: Guidelines for the analysis of free energy calculations. Journal of computer–aided molecular design 29(5), 397–411 (2015), DOI: 10.1007/s10822-015-9840-9
Kollman, P.: Free energy calculations: Applications to chemical and biochemical phenomena.
Chemical Reviews 93(7), 2395–2417 (1993), DOI: 10.1021/cr00023a004
Kutov, D.K., Katkova, E.V., Sulimov, E.V., et al.: Influence of the method of hydrogen atoms incorporation into the target protein on the protein–ligand binding energy. Bulletin of the South Ural State University, Ser. Mathematical Modelling, Programming & Computer Software 10(3), 94–107 (2017), DOI: 10.14529/mmp170308
Kutov, D.C., Sulimov, A.V., Sulimov, V.B.: Supercomputer docking: Investigation of low energy minima of protein–ligand complexes. Supercomputing Frontiers and Innovations 5(3), 134–137 (2018), DOI: 10.14529/jsfi180326
Lamim Ribeiro, J.a.M., Tiwary, P.: Toward achieving efficient and accurate ligand–protein unbinding with deep learning and molecular dynamics through RAVE. Journal of Chemical Theory and Computation 15(1), 708–719 (2019), DOI: 10.1021/acs.jctc.8b00869
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436 (2015), DOI: 10.1038/nature14539
Lushchekina, S.V., Makhaeva, G.F., Novichkova, D.A., et al.: Supercomputer modeling of dual–site acetylcholinesterase (AChE) inhibition. Supercomputing Frontiers and Innovations 5(4), 89–97 (2018), DOI: 10.14529/jsfi180410
McIntosh-Smith, S., Price, J., Sessions, R.B., et al.: High performance in silico virtual drug screening on many–core processors. The International Journal of High Performance Computing Applications 29(2), 119–134 (2015), DOI: 10.1177/1094342014528252
Michel, J., Foloppe, N., Essex, J.W.: Rigorous free energy calculations in structure–based drug design. Molecular Informatics 29(8-9), 570–578 (2010), DOI: 10.1002/minf.201000051
Mobley, D.L., Gilson, M.K.: Predicting binding free energies: Frontiers and benchmarks. Annual Review of Biophysics 46(1), 531–558 (2017), DOI: 10.1146/annurev-biophys-070816-033654
Morris, G.M., Huey, R., Lindstrom, W., et al.: Autodock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry 30(16), 2785–91 (2009), DOI: 10.1002/jcc.21256
Morris, G.M., Goodsell, D.S., Halliday, R.S., et al.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19(14), 1639–1662 (1998), DOI: 10.1002/(SICI)1096-987X(19981115)19:14¡1639::AID-JCC10¿3.0.CO;2-B
Neves, M.A., Totrov, M., Abagyan, R.: Docking and scoring with ICM: the benchmarking results and strategies for improvement. Journal of Computer–Aided Molecular Design 26(6), 675–686 (2012), DOI: 10.1007/s10822-012-9547-0
Nguyen, D.D., Wei, G.W.: Agl–score: Algebraic graph learning score for proteinligand binding scoring, ranking, docking, and screening. Journal of Chemical Information and Modeling 59(7), 3291–3304 (2019), DOI: 10.1021/acs.jcim.9b00334
Nogueira, M.S., Koch, O.: The development of target–specific machine learning models as scoring functions for docking–based target prediction. Journal of Chemical Information and Modeling 59(3), 1238–1252 (2019), DOI: 10.1021/acs.jcim.8b00773
Oferkin, I.V., Katkova, E.V., Sulimov, A.V., et al.: Evaluation of docking target functions by the comprehensive investigation of protein–ligand energy minima. Advances in Bioinformatics 2015, 126858 (2015), DOI: 10.1155/2015/126858
Oferkin, I.V., Zheltkov, D.A., Tyrtyshnikov, E.E., et al.: Evaluation of the docking algorithm based on Tensor Train global optimization. Bulletin of the South Ural State University, Ser. Mathematical Modelling, Programming & Computer Software 8(4), 83–99 (2015), DOI: 10.14529/mmp150407
Ohue, M., Shimoda, T., Suzuki, S., et al.: Megadock 4.0: an ultra–high–performance protein–protein docking software for heterogeneous supercomputers. Bioinformatics (Oxford, England) 30(22), 3281–3283 (2014), DOI: 10.1093/bioinformatics/btu532
Oseledets, I.: Tensor–train decomposition. SIAM Journal on Scientific Computing 33(5), 2295–2317 (2011), DOI: 10.1137/090752286
Oseledets, I., Tyrtyshnikov, E.: Breaking the curse of dimensionality, or how to use SVD in many dimensions. SIAM Journal on Scientific Computing 31(5), 3744–3759 (2009), DOI: 10.1137/090748330
Oseledets, I., Tyrtyshnikov, E.: TT–cross approximation for multidimensional arrays. Linear Algebra and its Applications 432(1), 70–88 (2010), DOI: 10.1016/j.laa.2009.07.024
Pagadala, N.S., Syed, K., Tuszynski, J.: Software for molecular docking: a review. Biophysical Reviews 9(2), 91–102 (2017), DOI: 10.1007/s12551-016-0247-1
Pan, A.C., Borhani, D.W., Dror, R.O., et al.: Molecular determinants of drugreceptor binding kinetics. Drug Discovery Today 18(13), 667–673 (2013), DOI: 10.1016/j.drudis.2013.02.007
Park, M.S., Gao, C., Stern, H.A.: Estimating binding affinities by docking/scoring methods using variable protonation states. Proteins: Structure, Function, and Bioinformatics 79(1), 304–314 (2011), DOI: 10.1002/prot.22883
Pei, J., Zheng, Z., Kim, H., et al.: Random forest refinement of pairwise potentials for proteinligand decoy detection. Journal of Chemical Information and Modeling 59(7), 3305–3315 (2019), DOI: 10.1021/acs.jcim.9b00356
Perthold, J.W., Oostenbrink, C.: Accelerated enveloping distribution sampling: Enabling sampling of multiple end states while preserving local energy minima. The Journal of Physical Chemistry B 122(19), 5030–5037 (2018), DOI: 10.1021/acs.jpcb.8b02725
Ragoza, M., Hochuli, J., Idrobo, E., et al.: Proteinligand scoring with convolutional neural networks. Journal of Chemical Information and Modeling 57(4), 942–957 (2017), DOI: 10.1021/acs.jcim.6b00740
Rarey, M., Kramer, B., Lengauer, T., et al.: A fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology 261(3), 470–489 (1996), DOI: 10.1006/jmbi.1996.0477
Rekapalli, B., Vose, A., Giblock, P.: HSPp–BLAST: Highly scalable parallel PSI–BLAST for very large–scale sequence searches. In: 4th International Conference on Bioinformatics and Computational Biology 2012, BICoB 2012, 12-14 March 2012, Las Vegas, Nevada, USA. pp. 37–42 (03 2012)
Ribeiro, J.M.L., Bravo, P., Wang, Y., et al.: Reweighted autoencoded variational Bayes for enhanced sampling (RAVE). The Journal of Chemical Physics 149(7), 072301 (2018), DOI: 10.1063/1.5025487
Riniker, S., Christ, C.D., Hansen, N., et al.: Comparison of enveloping distribution sampling and thermodynamic integration to calculate binding free energies of phenylethanolamine N–methyltransferase inhibitors. The Journal of Chemical Physics 135(2), 024105 (2011), DOI: 10.1063/1.3604534
Romanov, A.N., Jabin, S.N., Martynov, Y.B., et al.: Surface generalized born method: A simple, fast, and precise implicit solvent model beyond the coulomb approximation. The Journal of Physical Chemistry. A, Molecules, spectroscopy, kinetics, environment & general theory 108(43), 9323–9327 (2004), DOI: 10.1021/jp046721s
Ryde, U., Sderhjelm, P.: Ligand–binding affinity estimates supported by quantum–mechanical methods. Chemical Reviews 116(9), 5520–5566 (2016), DOI: 10.1021/acs.chemrev.5b00630
Sadovnichii, V.A., Sulimov, V.B.: Supercomputing technologies in medicine. In: Supercomputing Technologies in Science, pp. 16–23. Moscow University Publishing (2009)
Sinauridze, E.I., Romanov, A.N., Gribkova, I.V., et al.: New synthetic thrombin inhibitors: molecular design and experimental verification. PLoS One 6(5), e19969 (2011), DOI: 10.1371/journal.pone.0019969
Sliwoski, G., Kothiwale, S., Meiler, J., et al.: Computational methods in drug discovery. Pharmacological Reviews 66(1), 334–395 (2013), DOI: 10.1124/pr.112.007336
Sobolev, S.I.: Effective performance of distributed computing environments, pp. 249–258 (in Russian). Moscow State University Press (2008)
Spitaleri, A., Decherchi, S., Cavalli, A., et al.: Fast dynamic docking guided by adaptive electrostatic bias: The MD–binding approach. Journal of Chemical Theory and Computation 14(3), 1727–1736 (2018), DOI: 10.1021/acs.jctc.7b01088
Stewart, J.J.: Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re–optimization of parameters. Journal of Molecular Modeling 19(1), 1–32 (2013), DOI: 10.1007/s00894-012-1667-x
Stewart, J.J.P.: Mopac2016. http://OpenMOPAC.net (2016)
Still, W.C., Tempczyk, A., Hawley, R.C., et al.: Semianalytical treatment of solvation for molecular mechanics and dynamics. Journal of the American Chemical Society 112(16), 6127–6129 (1990), DOI: 10.1021/ja00172a038
Strecker, C., Meyer, B.: Plasticity of the binding site of renin: Optimized selection of protein structures for ensemble docking. Journal of Chemical Information and Modeling 58(5), 1121–1131 (2018), DOI: 10.1021/acs.jcim.8b00010
Sulimov, A., Kutov, D., Ilin, I., et al.: Supercomputer docking with a large number of degrees of freedom. In: Devillers, E.J., Geronikaki, A. (eds.) 10th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resourses 2019, CMPTI 2019, 23-27 June 2019, Ioannina, Greece. p. 24 (2019)
Sulimov, A.V., Kutov, D.C., Katkova, E.V., et al.: Combined docking with classical force field and quantum chemical semiempirical method PM7. Advances in Bioinformatics 2017, 7167691 (2017), DOI: 10.1155/2017/7167691
Sulimov, A.V., Kutov, D.C., Katkova, E.V., et al.: New generation of docking programs: Supercomputer validation of force fields and quantum–chemical methods for docking. Journal of Molecular Graphics and Modelling 78, 139–147 (2017), DOI: 10.1016/j.jmgm.2017.10.007
Sulimov, A.V., Kutov, D.C., Katkova, E.V., et al.: Search for approaches to improving the calculation accuracy of the proteinligand binding energy by docking. Russian Chemical Bulletin, International Edition 66(10), 1913–1924 (2017), DOI: 10.1007/s11172-017-1966-6
Sulimov, A.V., Kutov, D.C., Oferkin, I.V., et al.: Application of the docking program SOL for CSAR benchmark. Journal of Chemical Information and Modeling 53(8), 1946–56 (2013), DOI: 10.1021/ci400094h
Sulimov, A.V., Kutov, D.C., Sulimov, V.B.: Parallel supercomputer docking program of the new generation: Finding low energy minima spectrum. In: 4th Russian Supercomputing Days 2018, RuSCDays 2018, 2425 September 2018, Moscow, Russia. vol. 965, pp. 314–330 (2018), DOI: 10.1007/978-3-030-05807-4_27
Sulimov, A.V., Zheltkov, D.A., Oferkin, I.V., et al.: Evaluation of the novel algorithm of flexible ligand docking with moveable target–protein atoms. Computational and Structural Biotechnology Journal 15, 275–285 (2017), DOI: 10.1016/j.csbj.2017.02.004
Sulimov, A.V., Zheltkov, D.A., Oferkin, I.V., et al.: Tensor train global optimization: Application to docking in the configuration space with a large number of dimensions. In: 3rd Russian Supercomputing Days. vol. 793, pp. 151–167 (2017), DOI: 10.1007/978-3-319-
-0_12
Sulimov, V.B., Sulimov, A.: Docking: molecular modeling for drug discovery. AINTELL, Moscow (2017)
Sulimov, V.B., Kutov, D.C., Sulimov, A.V.: Advances in docking. Current Medicinal Chemistry 26(37), 1–25 (2019), DOI: 10.2174/0929867325666180904115000
Trager, R.E., Giblock, P., Soltani, S., et al.: Docking optimization, variance and promiscuity for large–scale drug–like chemical space using high performance computing architectures. Drug Discovery Today 21(10), 1672–1680 (2016), DOI: 10.1016/j.drudis.2016.06.023
Trott, O., Olson, A.J.: Autodock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry 31(2), 455–61 (2010), DOI: 10.1002/jcc.21334
Ustach, V.D., Lakkaraju, S.K., Jo, S., et al.: Optimization and evaluation of site–identification by ligand competitive saturation (SILCS) as a tool for target–based ligand optimization. Journal of Chemical Information and Modeling 59(6), 3018–3035 (2019), DOI: 10.1021/acs.jcim.9b00210
Voevodin, V.V., Antonov, A.S., Nikitenko, D.A., et al.: Supercomputer lomonosov–2: Large scale, deep monitoring and fine analytics for the user community. Supercomputing Frontiers and Innovations 6(2), 4–11 (2019), DOI: 10.14529/jsfi190201
Rezac, J., Hobza, P.: A halogen–bonding correction for the semiempirical PM6 method. Chemical Physics Letters 506(4), 286–289 (2011), DOI: 10.1016/j.cplett.2011.03.009
Rezac, J., Hobza, P.: Advanced corrections of hydrogen bonding and dispersion for semiempirical quantum mechanical methods. Journal of Chemical Theory and Computation 8(1), 141–151 (2012), DOI: 10.1021/ct200751e
Wang, J.C., Lin, J.H., Chen, C.M., et al.: Robust scoring functions for protein–ligand interactions with quantum chemical charge models. Journal of chemical information and modeling 51(10), 2528–2537 (2011), DOI: 10.1021/ci200220v
Wang, J., Wolf, R.M., Caldwell, J.W., et al.: Development and testing of a general amber force field. Journal of Computational Chemistry 25(9), 1157–1174 (2004), DOI: 10.1002/jcc.20035
Wang, L., Wu, Y., Deng, Y., et al.: Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free–energy calculation protocol and force field. Journal of the American Chemical Society 137(7), 2695–2703 (2015), DOI: 10.1021/ja512751q
Xie, B., Clark, J.D., Minh, D.D.L.: Efficiency of stratification for ensemble docking using reduced ensembles. Journal of Chemical Information and Modeling 58(9), 1915–1925 (2018), DOI: 10.1021/acs.jcim.8b00314
Yuriev, E., Holien, J., Ramsland, P.A.: Improvements, trends, and new ideas in molecular docking: 20122013 in review. Journal of Molecular Recognition 28(10), 581604 (2015), DOI: 10.1002/jmr.2471
Zhang, X., Wong, S.E., Lightstone, F.C.: Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines. Journal of Computational Chemistry 34(11), 915–927 (2013), DOI: 10.1002/jcc.23214
Zheltkov, D.A., Oferkin, I.V., Katkova, E.V., et al.: TTDock: a docking method based on tensor train decompositions. Numerical Methods and Programming 14(3), 279–291 (in Russian) (2013)
van Zundert, G., Rodrigues, J., Trellet, M., et al.: The HADDOCK2.2 Web Server: User–friendly integrative modeling of biomolecular complexes. Journal of Molecular Biology 428(4), 720–725 (2016), DOI: 10.1016/j.jmb.2015.09.014
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