mlsanityData to 7 Oct 2026

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

arXiv 2205.08306 ↗First posted 17 May 2022physics.chem-ph cs.LG q-bio.BMcited by 9

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Abstract (arXiv)

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations, offering similar accuracy as ab initio methods at orders-of-magnitude speedup. Until now, MLFFs mainly capture short-range interactions in small molecules or periodic materials, due to the increased complexity of constructing models and obtaining reliable reference data for large molecules, where long-ranged many-body effects become important. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on "bottom-up" and "top-down" molecular fragments of varying size, from which the relevant physicochemical interactions can be learned. GEMS is applied to study the dynamics of alanine-based peptides and the 46-residue protein crambin in aqueous solution, allowing nanosecond-scale MD simulations of >25k atoms at essentially ab initio quality. Our findings suggest that structural motifs in peptides and proteins are more flexible than previously thought, indicating that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes such as protein (mis)folding, drug-protein binding, or allosteric regulation.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 21 of its 139 references that are in the map. First posted on arXiv 17 May 2022.

Builds on

21 of 139 references.

References · 21

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  2. 2020Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densitiescited by 248
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9 more
  1. 2017Sparse learning of stochastic dynamic equationscited by 76
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Cited by year

Citing papers per year: 2022: 4; 2023: 5.

9 papers.

  1. 2022Structure-based drug design with geometric deep learningcited by 40
  2. 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
  3. 2023Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic sizecited by 33
  4. 2022SchNetPack 2.0: A neural network toolbox for atomistic machine learningcited by 29
  5. 2023Navigating protein landscapes with a machine-learned transferable coarse-grained modelcited by 22
  6. 2023Heat flux for semi-local machine-learning potentialscited by 3
  7. 2022Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergencecited by 2
  8. 2023Stress and heat flux via automatic differentiationcited by 1
  9. 2023From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fieldscited by 1