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NANO-D - 2018
NANO-D - 2018
Project-Team Nano-d
Team, Visitors, External Collaborators
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Partnerships and Cooperations
Dissemination
Bibliography
Project-Team Nano-d
Team, Visitors, External Collaborators
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Partnerships and Cooperations
Dissemination
Bibliography


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Bibliography

Major publications by the team in recent years
  • 1S. Grudinin, M. Garkavenko, A. Kazennov.

    Pepsi-SAXS : an adaptive method for rapid and accurate computation of small-angle X-ray scattering profiles, in: Acta Crystallographica Section D: Biological Crystallography, May 2017, vol. D73, pp. 449 - 464. [ DOI : 10.1107/S2059798317005745 ]

    https://hal.inria.fr/hal-01516719
  • 2A. Hoffmann, S. Grudinin.

    NOLB: Nonlinear Rigid Block Normal Mode Analysis Method, in: Journal of Chemical Theory and Computation, April 2017, vol. 13, no 5, pp. 2123-2134. [ DOI : 10.1021/acs.jctc.7b00197 ]

    https://hal.inria.fr/hal-01505843
  • 3L. Jaillet, S. Artemova, S. Redon.

    IM-UFF: extending the Universal Force Field for interactive molecular modeling, in: Journal of Molecular Graphics and Modelling, October 2017, vol. 77, pp. 350 - 362. [ DOI : 10.1016/j.jmgm.2017.08.023 ]

    https://hal.inria.fr/hal-01676519
  • 4M. Kadukova, S. Grudinin.

    Convex-PL: a novel knowledge-based potential for protein-ligand interactions deduced from structural databases using convex optimization, in: Journal of Computer-Aided Molecular Design, October 2017, vol. 31, no 10, pp. 943–958. [ DOI : 10.1007/s10822-017-0068-8 ]

    https://hal.inria.fr/hal-01591154
  • 5E. Neveu, D. Ritchie, P. Popov, S. Grudinin.

    PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation, in: Bioinformatics, August 2016, vol. 32, no 7, pp. i693-i701. [ DOI : 10.1093/bioinformatics/btw443 ]

    https://hal.archives-ouvertes.fr/hal-01358645
  • 6M. K. Nguyen, L. Jaillet, S. Redon.

    As-Rigid-As-Possible molecular interpolation paths, in: Journal of Computer-Aided Molecular Design, April 2017, vol. 31, no 4, pp. 403 - 417. [ DOI : 10.1007/s10822-017-0012-y ]

    https://hal.inria.fr/hal-01676132
  • 7G. Pagès, S. Grudinin.

    Analytical symmetry detection in protein assemblies. II. Dihedral and Cubic symmetries, in: Journal of Structural Biology, September 2018, vol. 203, no 3, pp. 185-194. [ DOI : 10.1016/j.jsb.2018.05.005 ]

    https://hal.inria.fr/hal-01816449
  • 8G. Pagès, E. Kinzina, S. Grudinin.

    Analytical symmetry detection in protein assemblies. I. Cyclic symmetries, in: Journal of Structural Biology, August 2018, vol. 203, no 2, pp. 142-148. [ DOI : 10.1016/j.jsb.2018.04.004 ]

    https://hal.inria.fr/hal-01779893
  • 9P. Popov, S. Grudinin.

    Knowledge of Native Protein–Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates, in: Journal of Chemical Information and Modeling, September 2015, vol. 55, no 10, pp. 2242–2255. [ DOI : 10.1021/acs.jcim.5b00372 ]

    https://hal.inria.fr/hal-01229886
  • 10D. W. Ritchie, S. Grudinin.

    Spherical polar Fourier assembly of protein complexes with arbitrary point group symmetry, in: Journal of Applied Crystallography, February 2016, vol. 49, no 1, pp. 158-167. [ DOI : 10.1107/S1600576715022931 ]

    https://hal.inria.fr/hal-01261402
Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 13G. Derevyanko, S. Grudinin, Y. Bengio, G. Lamoureux.

    Deep convolutional networks for quality assessment of protein folds, in: Bioinformatics, 2018, https://arxiv.org/abs/1801.06252 - 8 pages. [ DOI : 10.1093/bioinformatics/bty494 ]

    https://hal.inria.fr/hal-01702857
  • 14S. P. A. Edorh, S. Redon.

    Incremental update of electrostatic interactions in adaptively restrained particle simulations, in: Journal of Computational Chemistry, July 2018, vol. 39, no 20, pp. 1455-1469. [ DOI : 10.1002/jcc.25215 ]

    https://hal.inria.fr/hal-01761906
  • 15M. Kadukova, S. Grudinin.

    Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2, in: Journal of Computer-Aided Molecular Design, January 2018, vol. 32, no 1, pp. 151–162. [ DOI : 10.1007/s10822-017-0062-1 ]

    https://hal.inria.fr/hal-01591157
  • 16M. Karasikov, G. Pagès, S. Grudinin.

    Smooth orientation-dependent scoring function for coarse-grained protein quality assessment, in: Bioinformatics, December 2018, pp. 1-8. [ DOI : 10.1093/bioinformatics/bty1037 ]

    https://hal.inria.fr/hal-01971128
  • 17E. Neveu, P. Popov, A. Hoffmann, A. Migliosi, X. Besseron, G. Danoy, P. Bouvry, S. Grudinin.

    RapidRMSD: Rapid determination of RMSDs corresponding to motions of flexible molecules, in: Bioinformatics, August 2018, vol. 34, no 16, pp. 2757–2765. [ DOI : 10.1093/bioinformatics/bty160 ]

    https://hal.inria.fr/hal-01735214
  • 18M. K. Nguyen, L. Jaillet, S. Redon.

    ART-RRT: As-Rigid-As-Possible exploration of ligand unbinding pathways, in: Journal of Computational Chemistry, April 2018, vol. 39, no 11, pp. 665-678. [ DOI : 10.1002/jcc.25132 ]

    https://hal.inria.fr/hal-01973778
  • 19M. K. Nguyen, L. Jaillet, S. Redon.

    Generating conformational transition paths with low potential-energy barriers for proteins, in: Journal of Computer-Aided Molecular Design, August 2018, vol. 32, no 8, pp. 853-867. [ DOI : 10.1007/s10822-018-0137-7 ]

    https://hal.inria.fr/hal-01973757
  • 20G. Pagès, S. Grudinin.

    Analytical symmetry detection in protein assemblies. II. Dihedral and Cubic symmetries, in: Journal of Structural Biology, September 2018, vol. 203, no 3, pp. 185-194. [ DOI : 10.1016/j.jsb.2018.05.005 ]

    https://hal.inria.fr/hal-01816449
  • 21G. Pagès, E. Kinzina, S. Grudinin.

    Analytical symmetry detection in protein assemblies. I. Cyclic symmetries, in: Journal of Structural Biology, August 2018, vol. 203, no 2, pp. 142-148. [ DOI : 10.1016/j.jsb.2018.04.004 ]

    https://hal.inria.fr/hal-01779893
  • 22P. Popov, S. Grudinin.

    Eurecon: Equidistant Uniform Rigid-body Ensemble Constructor, in: Journal of Molecular Graphics and Modelling, March 2018, vol. 80, pp. 313-319. [ DOI : 10.1016/j.jmgm.2018.01.015 ]

    https://hal.inria.fr/hal-01702810
  • 23K. K. Singh, S. Redon.

    Single-pass Incremental Force Updates for Adaptively Restrained Molecular Dynamics, in: Journal of Computational Chemistry, March 2018, vol. 39, no 8, pp. 412-423. [ DOI : 10.1002/jcc.25126 ]

    https://hal.inria.fr/hal-01635863
  • 24G. Stoltz, Z. Trstanova.

    Langevin dynamics with general kinetic energies, in: Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, May 2018, vol. 16, no 2, pp. 777-806, https://arxiv.org/abs/1609.02891. [ DOI : 10.1137/16M110575X ]

    https://hal.archives-ouvertes.fr/hal-01364821

International Conferences with Proceedings

  • 25C. Guedj, L. Jaillet, F. Rousse, S. Redon.

    Atomistic Modelling and Simulation of Transmission Electron Microscopy Images: Application to Intrinsic Defects of Graphene, in: 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Porto, Portugal, Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, July 2018. [ DOI : 10.5220/0006829200150024 ]

    https://hal.inria.fr/hal-01973626

Conferences without Proceedings

  • 26C. Guedj, L. Jaillet, F. Rousse, S. Redon.

    Impact of Hydrogen on Graphene-based Materials: Atomistic Modeling and Simulation of HRSTEM Images, in: AVS 65th International Symposium & Exhibition, Long Beach, United States, October 2018.

    https://hal.inria.fr/hal-01973651

Other Publications

References in notes
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    https://www.ebi.ac.uk/pdbe/
  • 31B. Ahmadi, M. Kassiriha, K. Khodabakhshi, E. R. Mafi.

    Effect of nano layered silicates on automotive polyurethane refinish clear coat, in: Progress in Organic Coatings, 2007, vol. 60, no 2, pp. 99 - 104. [ DOI : 10.1016/j.porgcoat.2007.07.008 ]

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    The Cambridge Structural Database: a quarter of a million crystal structures and rising, in: Acta Crystallographica Section B, Jun 2002, vol. 58, no 3 Part 1, pp. 380–388.

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  • 33F. Ample, S. Ami, C. Joachim, F. Thiemann, G. Rapenne.

    A Morse manipulator molecule for the modulation of metallic shockley surface states, in: Chemical Physics Letters, 2007, vol. 434, pp. 280-285. [ DOI : 10.1016/j.cplett.2006.12.021 ]

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  • 34F. Ample, C. Joachim.

    A semi-empirical study of polyacene molecules adsorbed on a Cu(1 1 0) surface, in: Surface Science, 2006, vol. 600, no 16, pp. 3243 - 3251. [ DOI : 10.1016/j.susc.2006.06.015 ]

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  • 35A. Arkhipov, P. Freddolino, K. Imada, K. Namba, K. Schulten.

    Coarse-grained molecular dynamics simulations of a rotating bacterial flagellum, in: Biophysical Journal, 2006, vol. 91, pp. 4589-4597.
  • 36S. Artemova, S. Grudinin, S. Redon.

    A comparison of neighbor search algorithms for large rigid molecules, in: Journal of Computational Chemistry, 2011, vol. 32, no 13, pp. 2865–2877.

    http://dx.doi.org/10.1002/jcc.21868
  • 37S. Artemova, S. Redon.

    Adaptively Restrained Particle Simulations, in: Phys. Rev. Lett., Nov 2012, vol. 109.

    http://link.aps.org/doi/10.1103/PhysRevLett.109.190201
  • 38H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, P. E. Bourne.

    The Protein Data Bank, in: Nucleic Acids Research, 2000, vol. 28, no 1, pp. 235-242. [ DOI : 10.1093/nar/28.1.235 ]

    http://nar.oxfordjournals.org/content/28/1/235.abstract
  • 39X. Blanc, C. Le Bris, F. Legoll.

    Analysis of a prototypical multiscale method coupling atomistic and continuum mechanics, in: ESAIM: Mathematical Modelling and Numerical Analysis, 2005, vol. 39, no 04, pp. 797-826.

    http://dx.doi.org/10.1051/m2an:2005035
  • 40M. Bosson, S. Grudinin, X. Bouju, S. Redon.

    Interactive physically-based structural modeling of hydrocarbon systems, in: Journal of Computational Physics, 2012, vol. 231, no 6, pp. 2581 - 2598. [ DOI : 10.1016/j.jcp.2011.12.006 ]

    http://www.sciencedirect.com/science/article/pii/S0021999111007042
  • 41D. W. Brenner.

    Empirical potential for hydrocarbons for use in simulating the chemical vapor deposition of diamond films, in: Phys. Rev. B, Nov 1990, vol. 42, pp. 9458–9471.

    http://link.aps.org/doi/10.1103/PhysRevB.42.9458
  • 42T. Cagin, G. Wang, R. Martin, G. Zamanakos, N. Vaidehi, D. T. Mainz, W. A. Goddard III.

    Multiscale modeling and simulation methods with applications to dendritic polymers, in: Computational and Theoretical Polymer Science, 2001, vol. 11, no 5, pp. 345 - 356. [ DOI : 10.1016/S1089-3156(01)00026-5 ]

    http://www.sciencedirect.com/science/article/pii/S1089315601000265
  • 43E. Cances, F. Castella, P. Chartier, E. Faou, C. Le Bris, F. Legoll, G. Turinici.

    Long-time averaging for integrable Hamiltonian dynamics, in: Numerische Mathematik, 2005, vol. 100, pp. 211-232, 10.1007/s00211-005-0599-0.

    http://dx.doi.org/10.1007/s00211-005-0599-0
  • 44Q. Chaudhry, M. Scotter, J. Blackburn, B. Ross, A. Boxall, L. Castle, R. Aitken, R. Watkins.

    Applications and implications of nanotechnologies for the food sector, in: Food Additives & Contaminants: Part A, 2008, vol. 25, no 3, pp. 241-258.

    http://www.tandfonline.com/doi/abs/10.1080/02652030701744538
  • 45X. Chen, S. S. Mao.

    Titanium Dioxide Nanomaterials: Synthesis, Properties, Modifications, and Applications, in: ChemInform, 2007, vol. 38, no 41.

    http://dx.doi.org/10.1002/chin.200741216
  • 46S. Cooper, F. Khatib, A. Treuille, J. Barbero, J. Lee, M. Beenen, A. Leaver-Fay, D. Baker, Z. Popovic, F. Players.

    Predicting protein structures with a multiplayer online game, in: Nature, 2010, vol. 466, pp. 756-760.
  • 47M. Curreli, A. H. Nadershahi, G. Shahi.

    Emergence of nanomedical devices for the diagnosis and treatment of cancer: the journey from basic science to commercialization, in: International Journal of Technology Transfer and Commercialisation, 2008, vol. 7, no 4, pp. 290-307.
  • 48E. Darve.

    The Fast Multipole Method: Numerical Implementation, in: Journal of Computational Physics, 2000.
  • 49H. Dietz, S. M. Douglas, W. M. Shih.

    Folding DNA into Twisted and Curved Nanoscale Shapes, in: Science, 2009, vol. 325, no 5941, pp. 725-730. [ DOI : 10.1126/science.1174251 ]

    http://www.sciencemag.org/content/325/5941/725.abstract
  • 50S. J. Fleishman, T. A. Whitehead, D. C. Ekiert, C. Dreyfus, J. E. Corn, E.-M. Strauch, I. A. Wilson, D. Baker.

    Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin, in: Science, 2011, vol. 332, no 6031, pp. 816-821. [ DOI : 10.1126/science.1202617 ]

    http://www.sciencemag.org/content/332/6031/816.abstract
  • 51G. Fox-Rabinovich, B. Beake, K. Yamamoto, M. Aguirre, S. Veldhuis, G. Dosbaeva, A. Elfizy, A. Biksa, L. Shuster.

    Structure, properties and wear performance of nano-multilayered TiAlCrSiYN/TiAlCrN coatings during machining of Ni-based aerospace superalloys, in: Surface and Coatings Technology, 2010, vol. 204, pp. 3698 - 3706. [ DOI : 10.1016/j.surfcoat.2010.04.050 ]

    http://www.sciencedirect.com/science/article/pii/S0257897210003178
  • 52M. Goldberg, R. Langer, X. Jia.

    Nanostructured materials for applications in drug delivery and tissue engineering, in: Journal of Biomaterials Science, Polymer Edition, 2007, vol. 18, no 3, pp. 241-268. [ DOI : doi:10.1163/156856207779996931 ]

    https://doi.org/10.1163/156856207779996931
  • 53C. Guedj, L. Jaillet, F. Rousse, S. Redon.

    Atomistic Modelling and Simulation of Transmission Electron Microscopy Images: Application to Intrinsic Defects of Graphene, in: Proceedings of 8th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,, SciTePress, 2018, pp. 15-24.

    http://dx.doi.org/10.5220/0006829200150024
  • 54J.-H. He.

    An elementary introduction to recently developed asymptotic methods and nanomechanics in textile engineering, in: International Journal of Modern Physics B, 2008, vol. 22, no 21, pp. 3487-3578.
  • 55S. Helveg.

    Structure and Dynamics of Nanocatalysts, in: Microscopy and Microanalysis, 2010, vol. 16, no Supplement S2, pp. 1712-1713.

    http://dx.doi.org/10.1017/S1431927610055005
  • 56A. Heyden, D. G. Truhlar.

    Conservative Algorithm for an Adaptive Change of Resolution in Mixed Atomistic/Coarse-Grained Multiscale Simulations, in: Journal of Chemical Theory and Computation, 2008, vol. 4, no 2, pp. 217-221.

    http://pubs.acs.org/doi/abs/10.1021/ct700269m
  • 57A. Hoffmann, S. Grudinin.

    NOLB: Nonlinear Rigid Block Normal Mode Analysis Method, in: Journal of Chemical Theory and Computation, April 2017, vol. 13, no 5, pp. 2123-2134. [ DOI : 10.1021/acs.jctc.7b00197 ]

    https://hal.inria.fr/hal-01505843
  • 58A. Hoffmann, V. Perrier, S. Grudinin.

    A novel fast Fourier transform accelerated off-grid exhaustive search method for cryo-electron microscopy fitting, in: Journal of Applied Crystallography, August 2017, vol. 50, no 4, pp. 1036-1047. [ DOI : 10.1107/S1600576717008172 ]

    https://hal.inria.fr/hal-01553293
  • 59V. Hornak, R. Abel, A. Okur, B. Strockbine, A. Roitberg, C. Simmerling.

    Comparison of multiple Amber force fields and development of improved protein backbone parameters, in: Proteins: Structure, Function, and Bioinformatics, 2006, vol. 65, no 3, pp. 712–725.

    http://dx.doi.org/10.1002/prot.21123
  • 60C. Joachim, H. Tang, F. Moresco, G. Rapenne, G. Meyer.

    The design of a nanoscale molecular barrow, in: Nanotechnology, 2002, vol. 13, no 3, 330 p.

    http://stacks.iop.org/0957-4484/13/i=3/a=318
  • 61L. Kalé, R. Skeel, M. Bhandarkar, R. Brunner, A. Gursoy, N. Krawetz, J. Phillips, A. Shinozaki, K. Varadarajan, K. Schulten.

    NAMD2: Greater Scalability for Parallel Molecular Dynamics, in: Journal of Computational Physics, 1999, vol. 151, no 1, pp. 283 - 312. [ DOI : 10.1006/jcph.1999.6201 ]

    http://www.sciencedirect.com/science/article/pii/S0021999199962010
  • 62Z. Li, H. A. Scheraga.

    Monte Carlo-minimization approach to the multiple-minima problem in protein folding, in: Proceedings of the National Academy of Sciences, 1987, vol. 84, no 19, pp. 6611-6615.

    http://www.pnas.org/content/84/19/6611.abstract
  • 63L. Lo, Y. Li, K. Yeung, C. Yuen.

    Indicating the development stage of nanotechnology in the textile and clothing industry, in: International Journal of Nanotechnology, 2007, vol. 4, no 6, pp. 667-679.
  • 64W. Lu, C. M. Lieber.

    Nanoelectronics from the bottom up, in: Nature materials, 2007, vol. 6, no 11, pp. 841-850.
  • 65M. K. Nguyen, L. Jaillet, S. Redon.

    ART-RRT: As-Rigid-As-Possible exploration of ligand unbinding pathways, in: Journal of computational chemistry, 2018, vol. 39, no 11, pp. 665–678.
  • 66M. K. Nguyen, L. Jaillet, S. Redon.

    Generating conformational transition paths with low potential-energy barriers for proteins, in: Journal of Computer-Aided Molecular Design, 2018, vol. 32, no 8, pp. 853–867.
  • 67M. K. Nguyen.

    Efficient exploration of molecular paths from As-Rigid-As-Possible approaches and motion planning methods, Université Grenoble Alpes, 2018.
  • 68S. O. Nielsen, P. B. Moore, B. Ensing.

    Adaptive Multiscale Molecular Dynamics of Macromolecular Fluids, in: Phys. Rev. Lett., Dec 2010, vol. 105, 237802.

    http://link.aps.org/doi/10.1103/PhysRevLett.105.237802
  • 69A. Nikitin, X. Li, Z. Zhang, H. Ogasawara, H. Dai, A. Nilsson.

    Hydrogen Storage in Carbon Nanotubes through the Formation of Stable C-H Bonds, in: Nano Letters, 2008, vol. 8, no 1, pp. 162-167, PMID: 18088150.

    http://pubs.acs.org/doi/abs/10.1021/nl072325k
  • 70G. Pagès, S. Grudinin.

    Ananas : Analytical symmetry detection in protein assemblies, Inria, 2018.

    https://team.inria.fr/nano-d/software/ananas/
  • 71M. Praprotnik, L. Delle Site, K. Kremer.

    Adaptive resolution scheme for efficient hybrid atomistic-mesoscale molecular dynamics simulations of dense liquids, in: Phys. Rev. E, Jun 2006, vol. 73, 066701.

    http://link.aps.org/doi/10.1103/PhysRevE.73.066701
  • 72M. Praprotnik, S. Matysiak, L. D. Site, K. Kremer, C. Clementi.

    Adaptive resolution simulation of liquid water, in: Journal of Physics: Condensed Matter, 2007, vol. 19, no 29, 292201.

    http://stacks.iop.org/0953-8984/19/i=29/a=292201
  • 73M. Praprotnik, L. D. Site, K. Kremer.

    A macromolecule in a solvent: Adaptive resolution molecular dynamics simulation, in: The Journal of Chemical Physics, 2007, vol. 126, no 13, 134902.

    http://aip.scitation.org/doi/abs/10.1063/1.2714540?journalCode=jcp
  • 74M. Praprotnik, L. D. Site, K. Kremer.

    Multiscale Simulation of Soft Matter: From Scale Bridging to Adaptive Resolution, in: Annual Review of Physical Chemistry, 2008, vol. 59, no 1, pp. 545-571.

    http://www.annualreviews.org/doi/abs/10.1146/annurev.physchem.59.032607.093707
  • 75P. Procacci, T. Darden, M. Marchi.

    A Very Fast Molecular Dynamics Method To Simulate Biomolecular Systems with Realistic Electrostatic Interactions, in: The Journal of Physical Chemistry, 1996, vol. 100, no 24, pp. 10464-10468.

    http://pubs.acs.org/doi/abs/10.1021/jp960295w
  • 76X. Qian, T. Schlick.

    Efficient multiple-time-step integrators with distance-based force splitting for particle-mesh-Ewald molecular dynamics simulations, in: Journal of Chemical Physics, 2002, vol. 116, pp. 5971-5983.
  • 77D. W. Ritchie, G. J. Kemp.

    Protein docking using spherical polar Fourier correlations, in: Proteins: Structure, Function, and Bioinformatics, 2000, vol. 39, no 2, pp. 178–194.
  • 78M. C. Roco.

    The long view of nanotechnology development: the National Nanotechnology Initiative at 10 years, in: Journal of Nanoparticle Research, 2010.
  • 79B. Rooks.

    A shorter product development time with digital mock-up, in: Assembly Automation, 1998, vol. 18, no 1, pp. 34-38. [ DOI : doi:10.1108/01445159810201405 ]

    http://www.ingentaconnect.com/content/mcb/033/1998/00000018/00000001/art00004
  • 80R. Rossi, M. Isorce, S. Morin, J. Flocard, K. Arumugam, S. Crouzy, M. Vivaudou, S. Redon.

    Adaptive torsion-angle quasi-statics: a general simulation method with applications to protein structure analysis and design, in: Bioinformatics, 2007, vol. 23, no 13. [ DOI : 10.1093/bioinformatics/btm191 ]

    http://bioinformatics.oxfordjournals.org/content/23/13/i408.abstract
  • 81R. E. Rudd.

    Coarse-Grained Molecular Dynamics for Computer Modeling of Nanomechanical Systems, in: International Journal for Numerical Methods in Engineering, 2004.
  • 82A. Shih, P. Freddolino, A. Arkhipov, K. Schulten.

    Assembly of lipoprotein particles revealed by coarse-grained molecular dynamics simulations, in: Journal of Structural Biology, 2007, vol. 157, pp. 579-592.
  • 83Y. Shirai, A. J. Osgood, Y. Zhao, Y. Yao, L. Saudan, H. Yang, C. Yu-Hung, L. B. Alemany, T. Sasaki, J.-F. Morin, J. M. Guerrero, K. F. Kelly, J. M. Tour.

    Surface-Rolling Molecules, in: Journal of the American Chemical Society, 2006, vol. 128, no 14, pp. 4854-4864, PMID: 16594722.

    http://pubs.acs.org/doi/abs/10.1021/ja058514r
  • 84E. G. Stein, L. M. Rice, A. T. Brünger.

    Torsion-Angle Molecular Dynamics as a New Efficient Tool for NMR Structure Calculation, in: Journal of Magnetic Resonance, 1997, vol. 124, no 1, pp. 154 - 164. [ DOI : 10.1006/jmre.1996.1027 ]

    http://www.sciencedirect.com/science/article/pii/S1090780796910277
  • 85X. Sun, Z. Liu, K. Welsher, J. Robinson, A. Goodwin, S. Zaric, H. Dai.

    Nano-graphene oxide for cellular imaging and drug delivery, in: Nano Research, 2008, vol. 1, pp. 203-212, 10.1007/s12274-008-8021-8.

    http://dx.doi.org/10.1007/s12274-008-8021-8
  • 86D. Tomalia, L. Reyna, S. Svenson.

    Dendrimers as multi-purpose nanodevices for oncology drug delivery and diagnostic imaging, in: Biochemical Society Transactions, 2007, vol. 35, pp. 61-67.
  • 87N. Vaidehi, W. A. Goddard III.

    Domain Motions in Phosphoglycerate Kinase using Hierarchical NEIMO Molecular Dynamics Simulations, in: The Journal of Physical Chemistry A, 2000, vol. 104, no 11, pp. 2375-2383.

    http://pubs.acs.org/doi/abs/10.1021/jp991985d
  • 88C. Vanlerberghe.

    DeepMind, l'IA de Google, est championne en biologie moléculaire, Dec 2018.

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  • 89T. Vettorel, A. Y. Grosberg, K. Kremer.

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