Deep generative modeling of temperature-dependent structural ensembles of proteins Article Swipe
Giacomo Janson
,
Alexander Jussupow
,
Michael Feig
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.09.642148
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.03.09.642148
Deep learning has revolutionized protein structure prediction, but capturing conformational ensembles and structural variability remains an open challenge. While molecular dynamics (MD) is the foundation method for simulating biomolecular dynamics, it is computationally expensive. Recently, deep learning models trained on MD have made progress in generating structural ensembles at reduced cost. However, they remain limited in modeling atomistic details and, crucially, incorporating the effect of environmental factors. Here, we present aSAM (atomistic structural autoencoder model), a latent diffusion model trained on MD to generate heavy atom protein ensembles. Unlike most methods, aSAM models atoms in a latent space, greatly facilitating accurate sampling of side chain and backbone torsion angle distributions. Additionally, we extended aSAM into the first reported transferable generator conditioned on temperature, named aSAMt. Trained on the large and open mdCATH dataset, aSAMt captures temperature-dependent ensemble properties and demonstrates generalization beyond training temperatures. By comparing aSAMt ensembles to long MD simulations of fast folding proteins, we find that high-temperature training enhances the ability of deep generators to explore energy landscapes. Finally, we also show that our MD-based aSAMt can already capture experimentally observed thermal behavior of proteins. Our work is a step towards generalizable ensemble generation to complement physics- based approaches.
Related Topics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.03.09.642148
- https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.09.642148.full.pdf
- OA Status
- green
- Cited By
- 6
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408465033
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4408465033Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2025.03.09.642148Digital Object Identifier
- Title
-
Deep generative modeling of temperature-dependent structural ensembles of proteinsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-13Full publication date if available
- Authors
-
Giacomo Janson, Alexander Jussupow, Michael FeigList of authors in order
- Landing page
-
https://doi.org/10.1101/2025.03.09.642148Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.09.642148.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.09.642148.full.pdfDirect OA link when available
- Concepts
-
Generative grammar, Generative model, Artificial intelligence, Computer science, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6Per-year citation counts (last 5 years)
- References (count)
-
80Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4408465033 |
|---|---|
| doi | https://doi.org/10.1101/2025.03.09.642148 |
| ids.doi | https://doi.org/10.1101/2025.03.09.642148 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/40161645 |
| ids.openalex | https://openalex.org/W4408465033 |
| fwci | 16.09766777 |
| type | preprint |
| title | Deep generative modeling of temperature-dependent structural ensembles of proteins |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10044 |
| topics[0].field.id | https://openalex.org/fields/13 |
| topics[0].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1312 |
| topics[0].subfield.display_name | Molecular Biology |
| topics[0].display_name | Protein Structure and Dynamics |
| topics[1].id | https://openalex.org/T11162 |
| topics[1].field.id | https://openalex.org/fields/25 |
| topics[1].field.display_name | Materials Science |
| topics[1].score | 0.9902999997138977 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2505 |
| topics[1].subfield.display_name | Materials Chemistry |
| topics[1].display_name | Enzyme Structure and Function |
| topics[2].id | https://openalex.org/T10887 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9879000186920166 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Bioinformatics and Genomic Networks |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C39890363 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7245553731918335 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q36108 |
| concepts[0].display_name | Generative grammar |
| concepts[1].id | https://openalex.org/C167966045 |
| concepts[1].level | 3 |
| concepts[1].score | 0.47756463289260864 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5532625 |
| concepts[1].display_name | Generative model |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.42740464210510254 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.338703989982605 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C185592680 |
| concepts[4].level | 0 |
| concepts[4].score | 0.3293285667896271 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[4].display_name | Chemistry |
| keywords[0].id | https://openalex.org/keywords/generative-grammar |
| keywords[0].score | 0.7245553731918335 |
| keywords[0].display_name | Generative grammar |
| keywords[1].id | https://openalex.org/keywords/generative-model |
| keywords[1].score | 0.47756463289260864 |
| keywords[1].display_name | Generative model |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.42740464210510254 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.338703989982605 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/chemistry |
| keywords[4].score | 0.3293285667896271 |
| keywords[4].display_name | Chemistry |
| language | en |
| locations[0].id | doi:10.1101/2025.03.09.642148 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.09.642148.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2025.03.09.642148 |
| locations[1].id | pmid:40161645 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | bioRxiv : the preprint server for biology |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/40161645 |
| locations[2].id | pmh:oai:europepmc.org:10763337 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400806 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Europe PMC (PubMed Central) |
| locations[2].source.host_organization | https://openalex.org/I1303153112 |
| locations[2].source.host_organization_name | European Bioinformatics Institute |
| locations[2].source.host_organization_lineage | https://openalex.org/I1303153112 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11952339 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5079113446 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1757-4193 |
| authorships[0].author.display_name | Giacomo Janson |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I87216513 |
| authorships[0].affiliations[0].raw_affiliation_string | Michigan State University |
| authorships[0].institutions[0].id | https://openalex.org/I87216513 |
| authorships[0].institutions[0].ror | https://ror.org/05hs6h993 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I87216513 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Michigan State University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Giacomo Janson |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Michigan State University |
| authorships[1].author.id | https://openalex.org/A5045312855 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7851-2741 |
| authorships[1].author.display_name | Alexander Jussupow |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I87216513 |
| authorships[1].affiliations[0].raw_affiliation_string | Michigan State University |
| authorships[1].institutions[0].id | https://openalex.org/I87216513 |
| authorships[1].institutions[0].ror | https://ror.org/05hs6h993 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I87216513 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Michigan State University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Alexander Jussupow |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Michigan State University |
| authorships[2].author.id | https://openalex.org/A5061800523 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9380-6422 |
| authorships[2].author.display_name | Michael Feig |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I87216513 |
| authorships[2].affiliations[0].raw_affiliation_string | Michigan State University |
| authorships[2].institutions[0].id | https://openalex.org/I87216513 |
| authorships[2].institutions[0].ror | https://ror.org/05hs6h993 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I87216513 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Michigan State University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Michael Feig |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Michigan State University |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.09.642148.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep generative modeling of temperature-dependent structural ensembles of proteins |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10044 |
| primary_topic.field.id | https://openalex.org/fields/13 |
| primary_topic.field.display_name | Biochemistry, Genetics and Molecular Biology |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1312 |
| primary_topic.subfield.display_name | Molecular Biology |
| primary_topic.display_name | Protein Structure and Dynamics |
| related_works | https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W4391375266, https://openalex.org/W4365211920, https://openalex.org/W3014948380, https://openalex.org/W4391584540, https://openalex.org/W4380551139, https://openalex.org/W4317695495, https://openalex.org/W4395044357, https://openalex.org/W4287117424 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1101/2025.03.09.642148 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.09.642148.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2025.03.09.642148 |
| primary_location.id | doi:10.1101/2025.03.09.642148 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2025/03/13/2025.03.09.642148.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2025.03.09.642148 |
| publication_date | 2025-03-13 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3177828909, https://openalex.org/W4308463927, https://openalex.org/W4396721167, https://openalex.org/W4392544734, https://openalex.org/W2570104014, https://openalex.org/W3199721927, https://openalex.org/W4225552973, https://openalex.org/W4407280608, https://openalex.org/W2898210859, https://openalex.org/W4401895022, https://openalex.org/W2143592395, https://openalex.org/W2067236515, https://openalex.org/W2082127026, https://openalex.org/W3046751266, https://openalex.org/W1031578623, https://openalex.org/W6608557121, https://openalex.org/W4394008411, https://openalex.org/W3036167779, https://openalex.org/W4404826794, https://openalex.org/W4388870515, https://openalex.org/W4408293717, https://openalex.org/W4396723792, https://openalex.org/W4405036886, https://openalex.org/W2756533270, https://openalex.org/W4229457127, https://openalex.org/W4293353616, https://openalex.org/W2165373941, https://openalex.org/W4224035735, https://openalex.org/W4398257517, https://openalex.org/W4327719807, https://openalex.org/W4327550249, https://openalex.org/W4407169708, https://openalex.org/W4388947795, https://openalex.org/W2765322245, https://openalex.org/W2156084923, https://openalex.org/W2165414288, https://openalex.org/W4388870365, https://openalex.org/W1487052430, https://openalex.org/W2800697076, https://openalex.org/W1988624456, https://openalex.org/W4377019887, https://openalex.org/W2409341474, https://openalex.org/W2115540209, https://openalex.org/W2102245393, https://openalex.org/W4386765036, https://openalex.org/W2060757799, https://openalex.org/W4372330844, https://openalex.org/W2648949596, https://openalex.org/W1966387988, https://openalex.org/W2141179031, https://openalex.org/W2044705230, https://openalex.org/W4391431979, https://openalex.org/W4383957026, https://openalex.org/W4388464011, https://openalex.org/W2972246420, https://openalex.org/W4320040108, https://openalex.org/W4400231442, https://openalex.org/W4400475272, https://openalex.org/W2783073729, https://openalex.org/W4391383720, https://openalex.org/W4376226425, https://openalex.org/W4400932275, https://openalex.org/W3106745904, https://openalex.org/W4392271577, https://openalex.org/W4396895148, https://openalex.org/W2144288821, https://openalex.org/W2147988069, https://openalex.org/W3045213745, https://openalex.org/W2027189612, https://openalex.org/W2008708467, https://openalex.org/W3003257820, https://openalex.org/W4211196614, https://openalex.org/W2315297180, https://openalex.org/W2226825552, https://openalex.org/W2970971581, https://openalex.org/W3035965352, https://openalex.org/W2011301426, https://openalex.org/W4386585104, https://openalex.org/W3105774298, https://openalex.org/W3099878876 |
| referenced_works_count | 80 |
| abstract_inverted_index.a | 76, 96, 192 |
| abstract_inverted_index.By | 145 |
| abstract_inverted_index.MD | 41, 82, 151 |
| abstract_inverted_index.an | 16 |
| abstract_inverted_index.at | 49 |
| abstract_inverted_index.in | 45, 56, 95 |
| abstract_inverted_index.is | 23, 32, 191 |
| abstract_inverted_index.it | 31 |
| abstract_inverted_index.of | 65, 103, 153, 165, 187 |
| abstract_inverted_index.on | 40, 81, 122, 127 |
| abstract_inverted_index.to | 83, 149, 168, 198 |
| abstract_inverted_index.we | 69, 112, 157, 173 |
| abstract_inverted_index.Our | 189 |
| abstract_inverted_index.and | 12, 106, 130, 139 |
| abstract_inverted_index.but | 8 |
| abstract_inverted_index.can | 180 |
| abstract_inverted_index.for | 27 |
| abstract_inverted_index.has | 3 |
| abstract_inverted_index.our | 177 |
| abstract_inverted_index.the | 24, 63, 116, 128, 163 |
| abstract_inverted_index.(MD) | 22 |
| abstract_inverted_index.Deep | 1 |
| abstract_inverted_index.aSAM | 71, 92, 114 |
| abstract_inverted_index.also | 174 |
| abstract_inverted_index.and, | 60 |
| abstract_inverted_index.atom | 86 |
| abstract_inverted_index.deep | 36, 166 |
| abstract_inverted_index.fast | 154 |
| abstract_inverted_index.find | 158 |
| abstract_inverted_index.have | 42 |
| abstract_inverted_index.into | 115 |
| abstract_inverted_index.long | 150 |
| abstract_inverted_index.made | 43 |
| abstract_inverted_index.most | 90 |
| abstract_inverted_index.open | 17, 131 |
| abstract_inverted_index.show | 175 |
| abstract_inverted_index.side | 104 |
| abstract_inverted_index.step | 193 |
| abstract_inverted_index.that | 159, 176 |
| abstract_inverted_index.they | 53 |
| abstract_inverted_index.work | 190 |
| abstract_inverted_index.Here, | 68 |
| abstract_inverted_index.While | 19 |
| abstract_inverted_index.aSAMt | 134, 147, 179 |
| abstract_inverted_index.angle | 109 |
| abstract_inverted_index.atoms | 94 |
| abstract_inverted_index.based | 201 |
| abstract_inverted_index.chain | 105 |
| abstract_inverted_index.cost. | 51 |
| abstract_inverted_index.first | 117 |
| abstract_inverted_index.heavy | 85 |
| abstract_inverted_index.large | 129 |
| abstract_inverted_index.model | 79 |
| abstract_inverted_index.named | 124 |
| abstract_inverted_index.Unlike | 89 |
| abstract_inverted_index.aSAMt. | 125 |
| abstract_inverted_index.beyond | 142 |
| abstract_inverted_index.effect | 64 |
| abstract_inverted_index.energy | 170 |
| abstract_inverted_index.latent | 77, 97 |
| abstract_inverted_index.mdCATH | 132 |
| abstract_inverted_index.method | 26 |
| abstract_inverted_index.models | 38, 93 |
| abstract_inverted_index.remain | 54 |
| abstract_inverted_index.space, | 98 |
| abstract_inverted_index.Trained | 126 |
| abstract_inverted_index.ability | 164 |
| abstract_inverted_index.already | 181 |
| abstract_inverted_index.capture | 182 |
| abstract_inverted_index.details | 59 |
| abstract_inverted_index.explore | 169 |
| abstract_inverted_index.folding | 155 |
| abstract_inverted_index.greatly | 99 |
| abstract_inverted_index.limited | 55 |
| abstract_inverted_index.model), | 75 |
| abstract_inverted_index.present | 70 |
| abstract_inverted_index.protein | 5, 87 |
| abstract_inverted_index.reduced | 50 |
| abstract_inverted_index.remains | 15 |
| abstract_inverted_index.thermal | 185 |
| abstract_inverted_index.torsion | 108 |
| abstract_inverted_index.towards | 194 |
| abstract_inverted_index.trained | 39, 80 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.Finally, | 172 |
| abstract_inverted_index.However, | 52 |
| abstract_inverted_index.MD-based | 178 |
| abstract_inverted_index.accurate | 101 |
| abstract_inverted_index.backbone | 107 |
| abstract_inverted_index.behavior | 186 |
| abstract_inverted_index.captures | 135 |
| abstract_inverted_index.dataset, | 133 |
| abstract_inverted_index.dynamics | 21 |
| abstract_inverted_index.enhances | 162 |
| abstract_inverted_index.ensemble | 137, 196 |
| abstract_inverted_index.extended | 113 |
| abstract_inverted_index.factors. | 67 |
| abstract_inverted_index.generate | 84 |
| abstract_inverted_index.learning | 2, 37 |
| abstract_inverted_index.methods, | 91 |
| abstract_inverted_index.modeling | 57 |
| abstract_inverted_index.observed | 184 |
| abstract_inverted_index.physics- | 200 |
| abstract_inverted_index.progress | 44 |
| abstract_inverted_index.reported | 118 |
| abstract_inverted_index.sampling | 102 |
| abstract_inverted_index.training | 143, 161 |
| abstract_inverted_index.Recently, | 35 |
| abstract_inverted_index.atomistic | 58 |
| abstract_inverted_index.capturing | 9 |
| abstract_inverted_index.comparing | 146 |
| abstract_inverted_index.diffusion | 78 |
| abstract_inverted_index.dynamics, | 30 |
| abstract_inverted_index.ensembles | 11, 48, 148 |
| abstract_inverted_index.generator | 120 |
| abstract_inverted_index.molecular | 20 |
| abstract_inverted_index.proteins, | 156 |
| abstract_inverted_index.proteins. | 188 |
| abstract_inverted_index.structure | 6 |
| abstract_inverted_index.(atomistic | 72 |
| abstract_inverted_index.challenge. | 18 |
| abstract_inverted_index.complement | 199 |
| abstract_inverted_index.crucially, | 61 |
| abstract_inverted_index.ensembles. | 88 |
| abstract_inverted_index.expensive. | 34 |
| abstract_inverted_index.foundation | 25 |
| abstract_inverted_index.generating | 46 |
| abstract_inverted_index.generation | 197 |
| abstract_inverted_index.generators | 167 |
| abstract_inverted_index.properties | 138 |
| abstract_inverted_index.simulating | 28 |
| abstract_inverted_index.structural | 13, 47, 73 |
| abstract_inverted_index.approaches. | 202 |
| abstract_inverted_index.autoencoder | 74 |
| abstract_inverted_index.conditioned | 121 |
| abstract_inverted_index.landscapes. | 171 |
| abstract_inverted_index.prediction, | 7 |
| abstract_inverted_index.simulations | 152 |
| abstract_inverted_index.variability | 14 |
| abstract_inverted_index.biomolecular | 29 |
| abstract_inverted_index.demonstrates | 140 |
| abstract_inverted_index.facilitating | 100 |
| abstract_inverted_index.temperature, | 123 |
| abstract_inverted_index.transferable | 119 |
| abstract_inverted_index.Additionally, | 111 |
| abstract_inverted_index.environmental | 66 |
| abstract_inverted_index.generalizable | 195 |
| abstract_inverted_index.incorporating | 62 |
| abstract_inverted_index.temperatures. | 144 |
| abstract_inverted_index.conformational | 10 |
| abstract_inverted_index.distributions. | 110 |
| abstract_inverted_index.experimentally | 183 |
| abstract_inverted_index.generalization | 141 |
| abstract_inverted_index.revolutionized | 4 |
| abstract_inverted_index.computationally | 33 |
| abstract_inverted_index.high-temperature | 160 |
| abstract_inverted_index.temperature-dependent | 136 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5061800523 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I87216513 |
| citation_normalized_percentile.value | 0.9758729 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |