---
OA_place: publisher
OA_type: gold
_id: '22722'
abstract:
- lang: eng
  text: "The local structure of a protein strongly impacts its function and interactions\r\nwith
    other molecules. Representing local biomolecular environments remains a\r\nkey
    challenge while applying machine learning approaches over protein structures.
    The structural and chemical variability of these environments makes them\r\nchallenging
    to model, and performing representation learning on these objects\r\nremains largely
    under-explored. In this work, we propose representations for\r\nlocal protein
    environments that leverage intermediate features from machine learning force fields
    (MLFFs). We extensively benchmark state-of-the-art MLFFs,\r\ncomparing their performance
    across latent spaces and downstream tasks, and\r\nshow that their embeddings capture
    local structural (e.g., secondary motifs) and\r\nchemical features (e.g., amino
    acid identity and protonation state), organizing\r\nprotein environments into
    a structured manifold. We show that these representations enable zero-shot generalization
    and transfer across diverse downstream\r\ntasks. As a case study, we build a physics-informed,
    uncertainty-aware chemical shift predictor that achieves state-of-the-art accuracy
    in biomolecular NMR\r\nspectroscopy. Our results establish MLFFs as general-purpose,
    reusable representation learners for protein modeling, opening new directions
    in representation learning for structured physical systems. Code and data are
    available at\r\nhttps://github.com/mb012/MLFF_representation.\r\n"
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "This work was supported by the Institute of Science and Technology
  Austria (ISTA) through the IPC\r\ngrant “Generative Protein NMR” and by the Israeli
  Science Foundation (ISF) under grant number\r\n1834/24. This research used resources
  of the Institute of Science and Technology Austria’s scientific\r\ncomputing cluster.
  S.V. was supported in part by funding from the Eric and Wendy Schmidt Center at\r\nthe
  Broad Institute of MIT and Harvard."
article_processing_charge: No
arxiv: 1
author:
- first_name: Meital I
  full_name: Bojan, Meital I
  id: 11d88cf5-91ca-11f0-a95f-edf9f08f47b7
  last_name: Bojan
- first_name: Sanketh
  full_name: Vedula, Sanketh
  last_name: Vedula
- first_name: Sai A
  full_name: Maddipatla, Sai A
  id: e957f5e5-91c9-11f0-a95f-e090f66ecb4d
  last_name: Maddipatla
- first_name: Nadav E
  full_name: Sellam, Nadav E
  id: ef280fe0-91c9-11f0-a95f-8dea3f5bc513
  last_name: Sellam
- first_name: Anar
  full_name: Rzayev, Anar
  id: 2cd60677-9acd-11f1-ae1a-a85ae1c4dd35
  last_name: Rzayev
- first_name: Federico
  full_name: Napoli, Federico
  id: d42e08e7-f4fc-11eb-af0a-d71e26138f1b
  last_name: Napoli
  orcid: 0000-0002-9043-136X
- first_name: Paul
  full_name: Schanda, Paul
  id: 7B541462-FAF6-11E9-A490-E8DFE5697425
  last_name: Schanda
  orcid: 0000-0002-9350-7606
- first_name: Alexander
  full_name: Bronstein, Alexander
  id: 58f3726e-7cba-11ef-ad8b-e6e8cb3904e6
  last_name: Bronstein
  orcid: 0000-0001-9699-8730
citation:
  ama: 'Bojan MI, Vedula S, Maddipatla SA, et al. Representing local protein environments
    with machine learning force fields. In: <i>14th International Conference on Learning
    Representations</i>. Vol 2026. ; 2026:100760-100799.'
  apa: Bojan, M. I., Vedula, S., Maddipatla, S. A., Sellam, N. E., Rzayev, A., Napoli,
    F., … Bronstein, A. M. (2026). Representing local protein environments with machine
    learning force fields. In <i>14th International Conference on Learning Representations</i>
    (Vol. 2026, pp. 100760–100799). Rio de Janeiro, Brazil.
  chicago: Bojan, Meital I, Sanketh Vedula, Sai A Maddipatla, Nadav E Sellam, Anar
    Rzayev, Federico Napoli, Paul Schanda, and Alex M. Bronstein. “Representing Local
    Protein Environments with Machine Learning Force Fields.” In <i>14th International
    Conference on Learning Representations</i>, 2026:100760–99, 2026.
  ieee: M. I. Bojan <i>et al.</i>, “Representing local protein environments with machine
    learning force fields,” in <i>14th International Conference on Learning Representations</i>,
    Rio de Janeiro, Brazil, 2026, vol. 2026, pp. 100760–100799.
  ista: 'Bojan MI, Vedula S, Maddipatla SA, Sellam NE, Rzayev A, Napoli F, Schanda
    P, Bronstein AM. 2026. Representing local protein environments with machine learning
    force fields. 14th International Conference on Learning Representations. ICLR:
    International Conference on Learning Representations vol. 2026, 100760–100799.'
  mla: Bojan, Meital I., et al. “Representing Local Protein Environments with Machine
    Learning Force Fields.” <i>14th International Conference on Learning Representations</i>,
    vol. 2026, 2026, pp. 100760–99.
  short: M.I. Bojan, S. Vedula, S.A. Maddipatla, N.E. Sellam, A. Rzayev, F. Napoli,
    P. Schanda, A.M. Bronstein, in:, 14th International Conference on Learning Representations,
    2026, pp. 100760–100799.
conference:
  end_date: 2026-04-27
  location: Rio de Janeiro, Brazil
  name: 'ICLR: International Conference on Learning Representations'
  start_date: 2026-04-23
corr_author: '1'
das_tickbox: '1'
dataavailabilitystatement: "The code, trained models, and data-processing scripts
  are publicly available at https://github.\r\ncom/mb012/MLFF_representation. In addition,
  complete details of the models and optimization parameters are provided in Appendix
  G.3. The hardware resources used to produce the\r\nresults are specified in Appendix
  I.4. The loss functions, evaluation metrics, and details regarding\r\nablation studies
  are specified in Appendix G. These details ensure that all results reported in the
  paper\r\ncan be independently verified."
date_created: 2026-08-17T12:03:24Z
date_published: 2026-05-01T00:00:00Z
date_updated: 2026-08-18T06:36:55Z
day: '01'
ddc:
- '000'
- '570'
department:
- _id: GradSch
- _id: PaSc
- _id: AlBr
external_id:
  arxiv:
  - '2505.23354'
file:
- access_level: open_access
  checksum: 9f43f5469443388ec55388d95c4cf243
  content_type: application/pdf
  creator: dernst
  date_created: 2026-08-18T06:33:14Z
  date_updated: 2026-08-18T06:33:14Z
  file_id: '22723'
  file_name: 2026_ICLR_Bojan.pdf
  file_size: 8534339
  relation: main_file
  success: 1
file_date_updated: 2026-08-18T06:33:14Z
has_accepted_license: '1'
intvolume: '      2026'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 100760-100799
publication: 14th International Conference on Learning Representations
publication_status: published
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/mb012/MLFF_representation
researchdata_availability: yes
status: public
supplementarymaterial: yes
title: Representing local protein environments with machine learning force fields
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2026
year: '2026'
...