The DOI matches its Crossref record; 58 of the 60 references it cites were found in a registry or an index.
TorchMD: A Deep Learning Framework for Molecular Simulations
Open accessAbstract
Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All force computations including bond, angle, dihedral, Lennard-Jones, and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab initio potential, performing an end-to-end training, and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool set to support molecular simulations of machine learning potentials. Code and data are freely available at github.com/torchmd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The innovation in the development and integration of MD simulations with deep learning can reproduce, interpret, predict, and generate data relating to the behavior of biological macromolecules.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [188]
In the citing paper
“In another example, a deep learning framework with mixed classical and machine learning potentials (TorchMD) has been developed for molecular simulations.188”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [188]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J., Impey, R., & Klein, M. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 38,397
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB
Maier, J., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K., & Simmerling, C. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00255 · Cited by 11,141
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., & Adams, R. P. (2015) · arXiv · doi:10.48550/arxiv.1509.09292 · Cited by 2,628
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217
How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
Force Fields for Protein Simulations
Ponder, J. W., & Case, D. A. (2003) · Advances in protein chemistry · doi:10.1016/s0065-3233(03)66002-x · Cited by 1,914
A generalized reaction field method for molecular dynamics simulations
Tironi, I. G., Sperb, R. P., Smith, P. E., & A, W. F. V. G. (1995) · The Journal of Chemical Physics · doi:10.1063/1.469273 · Cited by 1,443
A Multiscale Coarse-Graining Method for Biomolecular Systems
Izvekov, S., & Voth, G. A. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp044629q · Cited by 1,210
Perspective on the Martini model
Marrink, . J., & Tieleman, D. P. (2013) · Chemical Society Reviews · doi:10.1039/c3cs60093a · Cited by 1,184
Identification of slow molecular order parameters for Markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990 · cited 3×
“The energy landscapes are obtained from binning the configurations over the first two TICA dimensions and computing the average of the equilibrium probability on each bin, obtained by Markov state model analysis of the mi-crostate of each configuration.”
MethodInfluentialSelf-citationACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale
Harvey, M. J., Giupponi, G., & Fabritiis, G. D. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9000685 · Cited by 916
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 907
Improving efficiency of large time-scale molecular dynamics simulations of hydrogen-rich systems
Feenstra, K. A., Hess, B., & Berendsen, H. J. C. (1999) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199906)20:8 · Cited by 894
How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization?
Piana, S., Lindorff‐Larsen, K., & Shaw, D. E. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2011.03.051 · Cited by 798
Identification of slow molecular order parameters for Markov model constructionPreprint
Perez-hern, G., Paul, F., Toni Giorgino, A., Fabritiis, G. D., Noé, F., & Contribution, E. (2013) · Cited by 657
Coarse-Graining Methods for Computational Biology
Saunders, M. G., & Voth, G. A. (2013) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-083012-130348 · Cited by 576
Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
Wang, J., Olsson, S., Wehmeyer, C., Pérez, A., Charron, N. E., Fabritiis, G. D., Noé, F., & Clementi, C. (2019) · ACS Central Science · doi:10.1021/acscentsci.8b00913 · Cited by 552
Self-citationThe TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Yao, K., Herr, J. E., Toth, D. W., Mckintyre, R., & Parkhill, J. (2018) · Chemical Science · doi:10.1039/c7sc04934j · Cited by 489
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
Schütt, K. T., Kessel, P., Gastegger, M., Nicoli, K. A., Tkatchenko, A., & Müller, K. R. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00908 · Cited by 453
Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset
Shirts, M. R., Klein, C., Swails, J., Yin, J., Gilson, M. K., Mobley, D. L., Case, D. A., & Zhong, E. D. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9977-1 · Cited by 382
Gao, X., Ramezanghorbani, F., Isayev, O., Smith, J. S., & Roitberg, A. E. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00451 · Cited by 353
Discovery Through the Computational Microscope
Lee, E. H., Hsin, J., Sotomayor, M. M., Comellas, G., & Schulten, K. J. (2009) · Structure · doi:10.1016/j.str.2009.09.001 · Cited by 338
Coarse-grained models of protein folding: toy models or predictive tools?
Clementi, C. (2007) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2007.10.005 · Cited by 335
Self-citationFCHL revisited: Faster and more accurate quantum machine learning
Christensen, A. S., Bratholm, L. A., Faber, F. A., & von Lilienfeld, O. A. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5126701 · Cited by 312
(1953) · Nature · doi:10.1038/171276b0 · Cited by 279
Coarse graining molecular dynamics with graph neural networks
Husic, B. E., Charron, N. E., Lemm, D., Wang, J., Pérez, A., Majewski, M., Krämer, A., Chen, Y., Olsson, S., Fabritiis, G. D., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0026133 · Cited by 222
Self-citationDeePCG: Constructing coarse-grained models via deep neural networks
Zhang, L., Han, J., Wang, H., Car, R., & Weinan, E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5027645 · Cited by 220
Escaping Atom Types in Force Fields Using Direct Chemical Perception
Mobley, D. L., Bannan, C. C., Rizzi, A., Bayly, C. I., Chodera, J. D., Lim, V. T., Lim, N. M., Beauchamp, K. A., Slochower, D. R., Shirts, M. R., et al. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00640 · Cited by 201
Coarse-graining auto-encoders for molecular dynamics
Wang, W., & Gómez‐Bombarelli, R. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0261-5 · Cited by 197
Crystal Structure of a Ten-Amino Acid Protein
Honda, S., Akiba, T., Kato, Y. S., Sawada, Y., Sekijima, M., Ishimura, M., Ooishi, A., Watanabe, H., Odahara, T., & Harata, K. (2008) · Journal of the American Chemical Society · doi:10.1021/ja8030533 · Cited by 188
High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing
Buch, I., Harvey, M., Giorgino, T., Anderson, D. P., & de Fabritiis, G. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900455r · Cited by 183
JAX, M.D. A framework for differentiable physics*
Schoenholz, S. S., & Cubuk, E. D. (2021) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac3ae9 · Cited by 182
On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations
Doerr, S., & Fabritiis, G. D. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct400919u · Cited by 175
Self-citationThe SIRAH 2.0 Force Field: Altius, Fortius, Citius
Machado, M. R., Barrera, E., Klein, F., Sóñora, M., Silva, S., & Pantano, S. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00006 · Cited by 164
Coarse-graining auto-encoders for molecular dynamics
Wang, W., & Gómez‐Bombarelli, R. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 164
Simple few-state models reveal hidden complexity in protein folding
Beauchamp, K. A., McGibbon, R., Lin, Y.-S., & Pande, V. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201810109 · Cited by 161
Coarse graining molecular dynamics with graph neural networks
Husic, B. E., Charron, N. E., Lemm, D., Wang, J., Pérez, A., Majewski, M., Krämer, A., Chen, Y., Olsson, S., Fabritiis, G. D., et al. (2020) · Refubium (Universitätsbibliothek der Freien Universität Berlin) · doi:10.17169/refubium-29706 · Cited by 151
Self-citationAutomated design of collective variables using supervised machine learning
Sultan, M. M., & Pande, V. S. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5029972 · Cited by 141
Variational selection of features for molecular kinetics
Scherer, M. K., Husic, B. E., Hoffmann, M., Paul, F., Wu, H., & Noé, F. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5083040 · Cited by 69
Optimized parameter selection reveals trends in Markov state models for protein folding
Husic, B. E., McGibbon, R. T., Sultan, M. M., & Pande, V. S. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4967809 · Cited by 64
Wang, W., Yang, T., Harris, W. H., & Gómez‐Bombarelli, R. (2020) · Chemical Communications · doi:10.1039/d0cc03512b · Cited by 63
Ruža, J., Wang, W., Schwalbe‐Koda, D., Axelrod, S., Harris, W. H., & Gómez‐Bombarelli, R. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0022431 · Cited by 61
Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach
Wang, J., Chmiela, S., Müller, K., Noé, F., & Clementi, C. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0007276 · Cited by 53
Self-citationModeling the mechanism of CLN025 beta-hairpin formation
McKiernan, K. A., Husic, B. E., & Pande, V. S. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4993207 · Cited by 46
Coarse-graining molecular systems by spectral matching
Nüske, F., Boninsegna, L., & Clementi, C. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5100131 · Cited by 43
Self-citationMartínez-Rosell, G., Giorgino, T., Harvey, M., & de Fabritiis, G. (2017) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026617666170414142549 · Cited by 33
Greener, J. G., & Jones, D. T. (2021) · PLOS ONE · doi:10.1371/journal.pone.0256990 · Cited by 29
Open Force Field Consortium: Escaping atom types using direct chemical perception with SMIRNOFF v0.1Preprint
Mobley, D., Bannan, C. C., Rizzi, A., Bayly, C., Chodera, J., Lim, V. T., Lim, N., Beauchamp, K., Shirts, M., Gilson, M., et al. (2018) · bioRxiv · doi:10.1101/286542 · Cited by 27
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure PythonPreprint
Schoenholz, S. S., & Cubuk, E. D. (2019) · arXiv (Cornell University) · Cited by 20
End-to-End Differentiable Molecular Mechanics Force Field ConstructionPreprint
Wang, Y., Fass, J., Kaminow, B., Herr, J. E., Rufa, D., Zhang, I., Pulido, I., Henry, M., & Chodera, J. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01196 · Cited by 7
Flagged first, then by citations
2 of the 60 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Duvenaud D. K., 2015, Advances in neural information processing systems, 2224
- Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; Chintala, S. In Advances in Neural Information Processing Systems 32; Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R., Eds. Curran Associates, Inc.: 2019; pp 8024–8035.
Cited by
Sorted by citations
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
Artificial intelligence for natural product drug discovery
Mullowney, M. W., Duncan, K., Elsayed, S. S., Garg, N., van der Hooft, J. J. J., Martin, N. I., Meijer, D., Terlouw, B. R., Biermann, F., Blin, K., et al. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00774-7 · Cited by 551
Bottom-up Coarse-Graining: Principles and Perspectives
Jin, J., Pak, A. J., Durumeric, A. E. P., Loose, T. D., & Voth, G. A. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00643 · Cited by 321
Machine learning for antimicrobial peptide identification and design
Wan, F., Wong, F. J., Collins, J. J., & de la Fuente‐Núñez, C. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00152-x · Cited by 317
Metadata
- Authors
- Doerr, S., Majewski, M., Pérez, A., Krämer, A., Clementi, C., Noe, F., Giorgino, T., & De Fabritiis, G.
- Affiliations
- Acellera (Spain) (ES)
- Universitat Pompeu Fabra (ES)
- Freie Universität Berlin (DE)
- Rice University (US)
- Department of Mathematics and Computer Science, Freie Universität, 14195 Berlin, Germany
- Biophysics Institute, National Research Council (CNR-IBF), 20133 Milano, Italy
- Acellera, 08005 Barcelona, Spain
- ORCID
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-03-17
- Volume
- 17
- Issue
- 4
- Pages
- 2355-2363
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC8486166
- OpenAlex
- W3113616921
- Cited by
- 230
- References
- 60
- Citations by year
- 2026: 30 · 2025: 51 · 2024: 52 · 2023: 61 · 2022: 24 · 2021: 12
- FWCI
- 11.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Theory and Computation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Chan Zuckerberg Initiative
- Horizon 2020 Framework Programme (823712)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- molecular dynamics, machine learning interatomic potentials, neural network potentials, Lennard-Jones interactions, electrostatic interactions, ab initio potentials, protein folding, coarse-grained models, PyTorch
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
