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.
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The innovation in development and integration of MD simulations with deep learning can reproduce, interpret, predict, and generate data relating to the behavior of biological macromolecules.
“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]
A deep learning framework with mixed classical and machine learning potentials (TorchMD) has been developed for molecular simulations.
“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]
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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
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 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
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 · see citing works
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 38,335 · see citing works
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
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 10,971 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 1,192 · see citing works
Perspective on the Martini model
Marrink, . J., & Tieleman, D. P. (2013) · Chemical Society Reviews · doi:10.1039/c3cs60093a · Cited by 1,172 · see citing works
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× · see citing works
“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., Giupponi, G., & de Fabritiis, G. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9000685 · Cited by 887 · see citing works
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4818908 · Cited by 885 · see citing works
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 · see citing works
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 · see citing works
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 567 · see citing works
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 519 · see citing works
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 475 · see citing works
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 434 · see citing works
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 · see citing works
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 339 · see citing works
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 · see citing works
Self-citationDiscovery 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 331 · see citing works
FCHL 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 304 · see citing works
(1953) · Nature · doi:10.1038/171276b0 · Cited by 279 · see citing works
DeePCG: 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 · see citing works
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 206 · see citing works
Self-citationCrystal 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 · see citing works
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 · see citing works
On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations.
Doerr, S., & de Fabritiis, G. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct400919u · Cited by 176 · see citing works
Self-citationCoarse-graining auto-encoders for molecular dynamics
Wang, W., & Gómez‐Bombarelli, R. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0261-5 · Cited by 176 · see citing works
Escaping atom types in force fields using direct chemical perception
Mobley, D., Bannan, C. C., Rizzi, A., Bayly, C., Chodera, J., Lim, V. T., Lim, N., Beauchamp, K., Slochower, D. R., Shirts, M., et al. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00640 · Cited by 174 · see citing works
The 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 · see citing works
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 · see citing works
Coarse-graining auto-encoders for molecular dynamics
Wang, W., & Gómez‐Bombarelli, R. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 160 · see citing works
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 · see citing works
Self-citationJAX, 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 150 · see citing works
Automated 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 137 · see citing works
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 · see citing works
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 63 · see citing works
Wang, W., Yang, T., Harris, W. H., & Gómez‐Bombarelli, R. (2020) · Chemical Communications · doi:10.1039/d0cc03512b · Cited by 63 · see citing works
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 · see citing works
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 52 · see citing works
Self-citationImproving 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 50 · see citing works
Modeling the mechanism of CLN025 beta-hairpin formation.
McKiernan, K., Husic, B., & Pande, V. (2017) · Journal of Chemical Physics · doi:10.1063/1.4993207 · Cited by 43 · see citing works
Coarse-graining molecular systems by spectral matching.
Nüske, F., Boninsegna, L., & Clementi, C. (2019) · Journal of Chemical Physics · doi:10.1063/1.5100131 · Cited by 35 · see citing works
Self-citationMartínez-Rosell, G., Giorgino, T., Harvey, M., & de Fabritiis, G. (2017) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026617666170414142549 · Cited by 33 · see citing works
Greener, J. G., & Jones, D. T. (2021) · PLOS ONE · doi:10.1371/journal.pone.0256990 · Cited by 29 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Flagged first, then by citations
2 of the 60 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- 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
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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 · see citing works
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 · see citing works
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 507 · see citing works
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 311 · see citing works
Machine learning for antimicrobial peptide identification and design
Wan, F.-P., Wong, F., Collins, J. J., & de la Fuente-Núñez, C. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00152-x · Cited by 278 · see citing works
Machine learning in protein structure prediction
AlQuraishi, M. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.005 · Cited by 251 · see citing works
The transformational role of GPU computing and deep learning in drug discovery
Pandey, M., Fernandez, M., Gentile, F., Isayev, O., Tropsha, A., Stern, A. C., & Cherkasov, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00463-x · Cited by 255 · see citing works
Mao, Q., Feng, M., Jiang, X. Z., Ren, Y., Luo, K. H., & van Duin, A. C. T. (2023) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101084 · Cited by 233 · see citing works
Bishara, D., Xie, Y., Liu, W. K., & Li, S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09795-8 · Cited by 228 · see citing works
AlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works
Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., Huang, J., Liu, H., & Yao, X. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1581 · Cited by 156 · see citing works
Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models
Noid, W. G. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c08731 · Cited by 145 · see citing works
Machine learning coarse-grained potentials of protein thermodynamics
Majewski, M., Pérez, A., Thölke, P., Doerr, S. H., Charron, N. E., Toni Giorgino, A., Husic, B. E., Clementi, C., Noé, F., & Fabritiis, G. D. (2023) · Nature Communications · doi:10.1038/s41467-023-41343-1 · Cited by 119 · see citing works
Self-citationMachine learning heralding a new development phase in molecular dynamics simulations
Prašnikar, E., Ljubič, M., Perdih, A., & Borišek, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10731-4 · Cited by 115 · see citing works
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Zeng, J., Zhang, D., Peng, A., Zhang, X., He, S., Wang, Y., Liu, X., Bi, H., Li, Y., Cai, C., et al. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00340 · Cited by 123 · see citing works
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
Thaler, S., & Zavadlav, J. (2021) · Nature Communications · doi:10.1038/s41467-021-27241-4 · Cited by 104 · see citing works
Galvelis, R., Varela‐Rial, A., Doerr, S., Fino, R., Eastman, P. K., Markland, T. E., Chodera, J. D., & Fabritiis, G. D. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00773 · Cited by 99 · see citing works
Self-citationArtificial Intelligence Enhanced Molecular Simulations
Zhang, J., Chen, D., Xia, Y., Huang, Y., Lin, X., Han, X., Ni, N., Wang, Z., Fan, Y., Yang, L., et al. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00214 · Cited by 96 · see citing works
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
Schütt, K. T., Hessmann, S. S. P., Gebauer, N. W. A., Lederer, J., & Gastegger, M. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0138367 · Cited by 98 · see citing works
Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation
Yan, X., Yue, T., Winkler, D. A., Yin, Y., Zhu, H., Jiang, G., & Yan, B. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00070 · Cited by 88 · see citing works
Jin, Z., & Wei, Z. (2023) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.13280 · Cited by 85 · see citing works
He, Y., Zhou, Y., Wen, T., Zhang, S., Huang, F., Zou, X., Ma, X., & Zhu, Y. (2022) · Applied Geochemistry · doi:10.1016/j.apgeochem.2022.105273 · Cited by 85 · see citing works
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Jones, D., Allen, J., Yang, Y., Bennett, W. F., Gokhale, M. B., Moshiri, N., & Rosing, T. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01214 · Cited by 75 · see citing works
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Zhang, X., Wang, L., Helwig, J., Luo, Y., Fu, C., Xie, Y., Liu, M., Lin, Y., Zhao, X., Yan, K., et al. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000115 · Cited by 123 · see citing works
Perspective: Machine learning in experimental solid mechanics
Brodnik, N. R., Muir, C., Tulshibagwale, N., Rossin, J., Echlin, M. P., Hamel, C. M., Kramer, S., Pollock, T. M., Kiser, J. D., Smith, C. W., et al. (2023) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2023.105231 · Cited by 69 · see citing works
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
Peláez, R. P., Simeon, G., Galvelis, R., Mirarchi, A., Eastman, P. K., Doerr, S., Thölke, P., Markland, T. E., & Fabritiis, G. D. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00253 · Cited by 64 · see citing works
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Open-Source Machine Learning in Computational Chemistry
Hagg, A., & Kirschner, K. N. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00643 · Cited by 62 · see citing works
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Zaverkin, V., Holzmüller, D., Christiansen, H., Errica, F., Alesiani, F., Takamoto, M., Niepert, M., & Kästner, J. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01254-1 · Cited by 57 · see citing works
Accurate machine learning force fields via experimental and simulation data fusion
Röcken, S., & Zavadlav, J. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01251-4 · Cited by 54 · see citing works
Applications and Advances in Machine Learning Force Fields
Wu, S., Yang, X., Zhao, X., Li, Z., Lü, M., Xie, X., & Yan, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00889 · Cited by 52 · see citing works
Coarse‐grained molecular dynamics simulation of polymers: Structures and dynamics
Shi, R., Qian, H., & Lu, Z. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1683 · Cited by 50 · see citing works
Deep coarse-grained potentials via relative entropy minimization
Thaler, S., Stupp, M., & Zavadlav, J. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0124538 · Cited by 49 · see citing works
Wang, X., Li, J., Yang, L., Chen, F., Wang, Y., Chang, J., Chen, J., Feng, W., Zhang, L., & Yu, K. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01297 · Cited by 49 · see citing works
Kanhaiya, K., Nathanson, M. H., Veld, P. J. I. ’., Zhu, C. Y., Nikiforov, I., Tadmor, E. B., Choi, Y. K., Im, W., Mishra, R. K., & Heinz, H. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00750 · Cited by 46 · see citing works
Graph-based molecular Pareto optimisation
Verhellen, J. (2022) · Chemical Science · doi:10.1039/d2sc00821a · Cited by 42 · see citing works
Differentiable simulation to develop molecular dynamics force fields for disordered proteins
Greener, J. G. (2024) · Chemical Science · doi:10.1039/d3sc05230c · Cited by 43 · see citing works
Modeling protein–ligand interactions for drug discovery in the era of deep learning
Wang, Y. X., Li, Y., Chen, J., & Lai, L. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00415b · Cited by 46 · see citing works
Thermodynamics-inspired explanations of artificial intelligence
Mehdi, S., & Tiwary, P. (2024) · Nature Communications · doi:10.1038/s41467-024-51970-x · Cited by 46 · see citing works
Tillotson, M. J., Diamantonis, N. I., Buda, C., Bolton, L. W., & Müller, E. A. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp05423j · Cited by 41 · see citing works
Artificial Intelligence for Conjugated Polymers
Yang, Q., Vriza, A., Rubio, C. A. C., Chan, H., Wu, Y., & Xu, J. (2024) · Chemistry of Materials · doi:10.1021/acs.chemmater.3c02358 · Cited by 43 · see citing works
Min, Y., Ye, W., Wang, P., Wang, X., Li, H., Wu, N., Bauer, S., Zheng, S., Shi, Y., Wang, Y., et al. (2024) · Advanced Science · doi:10.1002/advs.202405404 · Cited by 40 · see citing works
Son, A., Park, J., Kim, W., Yoon, Y., Lee, S., Park, Y., & Kim, H. (2024) · Molecules · doi:10.3390/molecules29194626 · Cited by 42 · see citing works
Progress and Challenges of Additive Manufacturing of Tungsten and Alloys as Plasma-Facing Materials
Howard, L., Parker, G. D., & Yu, X. (2024) · Materials · doi:10.3390/ma17092104 · Cited by 37 · see citing works
Review of External Field Effects on Electrocatalysis: Machine Learning Guided Design
Wang, L., Zhou, X., Luo, Z., Liu, S., Yue, S., Chen, Y., & Liu, Y. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202408870 · Cited by 35 · see citing works
On the design space between molecular mechanics and machine learning force fields
Wang, Y., Takaba, K., Chen, M. S., Wieder, M., Xu, Y., Zhu, T., Zhang, J. Z. H., Nagle, A., Yu, K., Wang, X., et al. (2025) · Applied Physics Reviews · doi:10.1063/5.0237876 · Cited by 39 · see citing works
Learning pair potentials using differentiable simulations
Wang, W., Wu, Z., Dietschreit, J. C. B., & Gómez‐Bombarelli, R. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0126475 · Cited by 31 · see citing works
Computational insight into stability-enhanced systems of anthocyanin with protein/peptide
Cheng, X., Chen, P., & Zhang, L. (2023) · Food Chemistry Molecular Sciences · doi:10.1016/j.fochms.2023.100168 · Cited by 30 · see citing works
Greener, J. G., & Jones, D. T. (2021) · PLOS ONE · doi:10.1371/journal.pone.0256990 · Cited by 29 · see citing works
Protein Function Analysis through Machine Learning
Avery, C., Patterson, J., Grear, T. J., Frater, T. D., & Jacobs, D. J. (2022) · Biomolecules · doi:10.3390/biom12091246 · Cited by 29 · see citing works
Stierle, R., Bauer, G., Thiele, N., Bursik, B., Rehner, P., & Groß, J. (2024) · Chemical Engineering Science · doi:10.1016/j.ces.2024.120380 · Cited by 27 · see citing works
From GPUs to AI and quantum: three waves of acceleration in bioinformatics
Schmidt, B., & Hildebrandt, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103990 · Cited by 28 · see citing works
Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy
Mayr, F., Harth, M., Kouroudis, I., Rinderle, M., & Gagliardi, A. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c04223 · Cited by 26 · see citing works
dxtb —An efficient and fully differentiable framework for extended tight-binding
Friede, M., Hölzer, C., Ehlert, S., & Grimme, S. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0216715 · Cited by 28 · see citing works
Coupling streaming AI and HPC ensembles to achieve 100–1000× faster biomolecular simulations
Brace, A., Yakushin, I., Ma, H., Trifan, A., Munson, T., Foster, I. T., Ramanathan, A., Lee, H., Turilli, M., & Jha, S. (2022) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps53621.2022.00083 · Cited by 25 · see citing works
Liu, J., Zhang, T., & Sun, S. (2023) · Geoscience Frontiers · doi:10.1016/j.gsf.2023.101735 · Cited by 24 · see citing works
Top-Down Machine Learning of Coarse-Grained Protein Force Fields
Navarro, C., Majewski, M., & de Fabritiis, G. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00638 · Cited by 25 · see citing works
Self-citationMachine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
Matin, S., Allen, A. E. A., Smith, J. S., Lubbers, N., Jadrich, R. B., Messerly, R. A., Nebgen, B. T., Li, Y. W., Tretiak, S., & Barros, K. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01051 · Cited by 24 · see citing works
Nandi, S., Bhaduri, S., Das, D., Ghosh, P., Mandal, M., & Mitra, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01161 · Cited by 23 · see citing works
Wang, T., He, X., Li, M., Shao, B., & Liu, T. (2023) · Scientific Data · doi:10.1038/s41597-023-02465-9 · Cited by 25 · see citing works
Integrated computational approaches for advancing antimicrobial peptide development
Fang, Y., Ma, Y., Yu, K., Dong, J. N., & Zeng, W. (2024) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2024.09.011 · Cited by 22 · see citing works
Advancing chemical engineering technology with artificial intelligence
Ding, C., Gui, X., & Jiang, J. (2025) · Clean Energy · doi:10.1093/ce/zkaf036 · Cited by 24 · see citing works
Cioni, M., Polino, D., Rapetti, D., Pesce, L., Piane, M. D., & Pavan, G. M. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0139010 · Cited by 22 · see citing works
chemtrain: Learning deep potential models via automatic differentiation and statistical physics
Fuchs, P., Thaler, S., Röcken, S., & Zavadlav, J. (2025) · Computer Physics Communications · doi:10.1016/j.cpc.2025.109512 · Cited by 21 · see citing works
Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?
Gianti, E., & Percec, S. (2022) · Biomacromolecules · doi:10.1021/acs.biomac.1c01436 · Cited by 20 · see citing works
Data science techniques in biomolecular force field development
Ding, Y., Yu, K., & Huang, J. (2022) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2022.102502 · Cited by 21 · see citing works
BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening
Brocidiacono, M., Francoeur, P. G., Aggarwal, R., Popov, K. I., Koes, D. R., & Tropsha, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01211 · Cited by 21 · see citing works
What can molecular assembly learn from catalysed assembly in living organisms?
Lei, Z., Wang, X., Yang, L., Qu, H., Sun, Y., Yang, Y., Li, W., Zhang, W., Cao, X., Fan, C., et al. (2024) · Chemical Society Reviews · doi:10.1039/d3cs00634d · Cited by 21 · see citing works
Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery
Bernardi, A., Bennett, W. F., He, S., Jones, D., Kirshner, D. A., Bennion, B. J., & Carpenter, T. S. (2023) · Membranes · doi:10.3390/membranes13110851 · Cited by 20 · see citing works
Molecular dynamics-driven drug discovery
Yan, D., Ma, Y., Chen, X., Shi, D., & Wang, Q. (2025) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp00380f · Cited by 20 · see citing works
Xu, M., Zhu, T., & Zhang, J. Z. H. (2021) · Frontiers in Chemistry · doi:10.3389/fchem.2021.692200 · Cited by 19 · see citing works
Ab initio dispersion potentials based on physics-based functional forms with machine learning
Villot, C., & Lao, K. U. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0204064 · Cited by 19 · see citing works
Quach, C. D., Gilmer, J., Pert, D., Mason-Hogans, A., Iacovella, C. R., Cummings, P. T., & McCabe, C. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0080838 · Cited by 17 · see citing works
Current and future machine learning approaches for modeling atmospheric cluster formation
Kubečka, J., Knattrup, Y., Engsvang, M., Jensen, A. B., Ayoubi, D., Wu, H., Christiansen, O., & Elm, J. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00435-0 · Cited by 18 · see citing works
Han, B., & Yu, K. (2025) · Nature Communications · doi:10.1038/s41467-025-56061-z · Cited by 18 · see citing works
On the application of artificial intelligence in virtual screening
Thaingtamtanha, T., Ravichandran, R., & Gentile, F. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2025.2508866 · Cited by 17 · see citing works
Automatic differentiation for orbital-free density functional theory
Tan, C. W., Pickard, C. J., & Witt, W. C. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0138429 · Cited by 17 · see citing works
GPU-accelerated artificial neural network potential for molecular dynamics simulation
Zhang, M., Hibi, K., & Inoue, J. (2023) · Computer Physics Communications · doi:10.1016/j.cpc.2022.108655 · Cited by 16 · see citing works
Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases
Backenköhler, M., Groß, J., Wolf, V., & Volkamer, A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00055 · Cited by 16 · see citing works
QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials
Zariquiey, F. S., Farr, S. E., Doerr, S. H., & Fabritiis, G. D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00033 · Cited by 16 · see citing works
Self-citationThe continuous evolution of biomolecular force fields
Lu, X., Chen, J., & Huang, J. (2025) · Structure · doi:10.1016/j.str.2025.05.013 · Cited by 15 · see citing works
Katzberger, P., & Riniker, S. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0147027 · Cited by 15 · see citing works
Application of computational approaches in biomembranes: From structure to function
Guo, J., Bao, Y., Li, M., Li, S., Xi, L., Xin, P., Wu, L., Liu, H., & Mu, Y. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1679 · Cited by 15 · see citing works
Computational methods in glaucoma research: Current status and future outlook
Kim, M. J., Martin, C. A., Kim, J., & Jablonski, M. M. (2023) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2023.101222 · Cited by 15 · see citing works
Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics
Carrer, M., Cezar, H. M., Bore, S. L., Ledum, M., & Cascella, M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00564 · Cited by 15 · see citing works
Molecular Dynamics Simulations on Cloud Computing and Machine Learning Platforms
Sharma, P., & Jadhao, V. (2021) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud53861.2021.00101 · Cited by 15 · see citing works
Differentiable Matrix Elements with MadJax
Heinrich, L., & Kagan, M. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2438/1/012137 · Cited by 15 · see citing works
A maximum caliber approach for continuum path ensembles
Bolhuis, P., Brotzakis, Z. F., & Vendruscolo, M. (2021) · European Physical Journal B : Condensed Matter Physics · doi:10.1140/epjb/s10051-021-00154-3 · Cited by 19 · see citing works
Opportunities and Challenges for In Silico Drug Discovery at Delta Opioid Receptors
Meqbil, Y. J., & van Rijn, R. M. (2022) · Pharmaceuticals · doi:10.3390/ph15070873 · Cited by 14 · see citing works
Zou, G., Sow, C. H., Wang, Z., Chen, X., & Gao, H. (2024) · ACS Nano · doi:10.1021/acsnano.4c03194 · Cited by 15 · see citing works
Hu, Q., Johannesen, A., Graham, D. S., & Goodpaster, J. D. (2023) · Digital Discovery · doi:10.1039/d3dd00051f · Cited by 13 · see citing works
Töpfer, K., Vazquez-Salazar, L. I., & Meuwly, M. (2024) · Computer Physics Communications · doi:10.1016/j.cpc.2024.109446 · Cited by 13 · see citing works
Fuchs, P., Chen, W., Thaler, S., & Zavadlav, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00996 · Cited by 13 · see citing works
Cheng, S., Chen, Y., Cao, Y. H., Huang, X., Liu, B., Liu, M., Jiang, S., & Jiang, L. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.105216 · Cited by 13 · see citing works
High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks
Winkler, L., Müller, K., & Sauceda, H. E. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ac6ec6 · Cited by 13 · see citing works
Baltrukevich, H., & Podlewska, S. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.844293 · Cited by 12 · see citing works
Khanna, A., Shedge, S. V., Zuehlsdorff, T. J., & Isborn, C. M. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0217080 · Cited by 12 · see citing works
- 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
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