The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index; 9 of the 16 sources its claims cite were checked for support.
Evolutionary-scale prediction of atomic level protein structure with a language model
Open accessAbstract
Artificial intelligence has the potential to open insight into the structure of proteins at the scale of evolution. It has only recently been possible to extend protein structure prediction to two hundred million cataloged proteins. Characterizing the structures of the exponentially growing billions of protein sequences revealed by large scale gene sequencing experiments would necessitate a break-through in the speed of folding. Here we show that direct inference of structure from primary sequence using a large language model enables an order of magnitude speed-up in high resolution structure prediction. Leveraging the insight that language models learn evolutionary patterns across millions of sequences, we train models up to 15B parameters, the largest language model of proteins to date. As the language models are scaled they learn information that enables prediction of the three-dimensional structure of a protein at the resolution of individual atoms. This results in prediction that is up to 60x faster than state-of-the-art while maintaining resolution and accuracy. Building on this, we present the ESM Metage-nomic Atlas. This is the first large-scale structural characterization of metagenomic proteins, with more than 617 million structures. The atlas reveals more than 225 million high confidence predictions, including millions whose structures are novel in comparison with experimentally determined structures, giving an unprecedented view into the vast breadth and diversity of the structures of some of the least understood proteins on earth.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper introduces ESM-2 protein language models up to 15B parameters and ESMFold, an end-to-end single-sequence structure predictor that reaches atomic-resolution accuracy while being up to 60x faster than state-of-the-art pipelines. It uses ESMFold to build the ESM Metagenomic Atlas, folding over 617 million MGnify90 sequences and identifying more than 225 million high-confidence structures, many novel relative to known sequences and structures.
Key findings
- Scaling ESM-2 from 8M to 15B parameters improves language modeling and structural information: validation perplexity falls from 10.45 to 6.37, unsupervised long-range contact precision (LR P@L) rises from 0.16 to 0.54, and structure-module-only LDDT rises from 0.48 to 0.72. The 15B model reaches TM-score 0.71 on CAMEO and 0.54 on CASP14, 0.064 higher than the 150M ESM-2 model on both.
- ESMFold achieves average TM-score 0.83 on CAMEO and 0.68 on CASP14, compared with AlphaFold2 at 0.88 and 0.85 and RosettaFold at 0.82 on CAMEO. On a single NVIDIA V100 GPU, ESMFold predicts a 384-residue protein in 14.2 seconds, 6x faster than a single AlphaFold2 model, with up to ~60x speedup on shorter sequences; MSA search alone can take over 10 minutes.
- The ESM Metagenomic Atlas folds over 617 million MGnify90 sequences in 2 weeks on ~2,000 GPUs, yielding ~365 million good-confidence predictions (mean pLDDT > 0.5 and pTM > 0.5) and ~225 million high-confidence predictions (mean pLDDT > 0.7 and pTM > 0.7). In a 1-million high-confidence sample, 76.8% have <90% sequence identity to UniRef90, 12.6% have no PDB structure with TM-score > 0.5, and 10.4% have both low structural similarity (TM-score ≤ 0.5) and no close sequence homolog (<30% identity).
- Confidence is well calibrated: for high-confidence predictions (pLDDT > 0.7), CAMEO accuracy is comparable to AlphaFold2 (ESMFold LDDT = 0.83, AlphaFold2 LDDT = 0.85); for very high confidence (pLDDT > 0.9), median all-atom RMSD95 is 1.42 Å and backbone RMSD95 is 0.94 Å.
Methods
- ESM-2 is a BERT-style transformer masked language model trained on UniRef50/UniRef90 sequences, scaled from 8M to 15B parameters, with models trained for up to 500K updates (15B for 270K updates) and evaluated on held-out UniRef50 clusters.
- ESMFold adds a 48-block folding head to a frozen 3B-parameter ESM-2, trained on ~25K PDB clusters covering ~325K experimentally determined chains plus ~12M AlphaFold2-predicted structures; evaluation uses temporally held-out CAMEO (194 proteins) and CASP14 (51 targets) test sets.
- Metagenomic analysis folded MGnify90 sequences of length 20–1024, covering 99% of the database, and used Foldseek, MMseqs2, blastp, and UMAP for structural and sequence similarity analyses.
Limitations
- The paper states that ESMFold was not trained on protein complexes, so complex prediction is out of distribution; it also notes that ablating MSAs for AlphaFold and RosettaFold is an artificial single-sequence setting because those models were not explicitly trained for single sequences.
- The paper states that calibration becomes critical as structure prediction scales, and that current models are far from the limit of scale in parameters, sequence data, and compute.
- Evident from the text: this is a bioRxiv preprint not certified by peer review; ESMFold is less accurate than AlphaFold2 overall on CAMEO (0.83 vs 0.88 TM-score) and CASP14 (0.68 vs 0.85), with a larger gap on hard CAMEO targets (0.45 vs 0.62).
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Claims the paper makes
9 of 16 cited sources checked · 5 supported · 0 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“the vast majority (76.8%) of high confidence predictions being separate from UniRef90 (32) by at least 90% sequence identity”
What the sources say
- Supportedcites [32] source
The citation is attached to UniRef90, identifying the reference database used for the citing paper’s comparison. The Introduction describes UniRef90 as clustering sequences at the 90% identity level; the 76.8% figure is the citing paper’s own result, not a result attributed to this source. (LLM, conf=0.90)
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In the paper
“Very high confidence predictions in the metagenomic atlas are expected to often be reliable at a resolution sufficient for insight similar to experimentally determined structures, such as into the biochemistry of active sites (61);”
What the sources say
- Partly supportedcites [61] sourceExperimental — accuracy not yet measured
The High-resolution models section says predicted models can meet demanding structural requirements and sometimes support ligand-binding studies; the Medium-resolution models section says predictions can help locate active sites. The paper does not assess predictions in the metagenomic atlas or establish that its very high-confidence predictions are often reliable enough to provide experimentally comparable insight into active-site biochemistry. Page numbers are unavailable in our copy. (LLM, conf=0.85)
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“We fold over 617 million sequences from the MGnify90 database (25).”
What the sources say
- Partly supportedcites [25] sourceExperimental — accuracy not yet measured
The 'UPDATES TO THE MGnify PROTEIN DATABASE' section describes MGnify protein sequences clustered at 90% identity, supporting the identification of the database. It reports over 1.1 billion sequences in the full database, but does not give a count of 617 million for the 90%-clustered set. The citing authors' folding of those sequences is their own work, not a finding attributed to this source. (LLM, conf=0.84)
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Every source the paper cites for this claim was checked and supports it.
In the paper
“In a comparison with AlphaFold-Multimer (45) on a dataset of 2,978 recent multimeric complexes deposited in the PDB, ESMFold achieves the same qualitative DockQ (39) categorization for 53.2% of chain pairs, despite not being trained on protein complexes (Fig. S6).”
What the sources say
- Supportedcites [45] Richard Evans , Michael O’Neill , Alexander Pritzel , Natasha Antropova , Andrew Senior , Tim Green , Augustin Žídek , Russ Bates , Sam Blackwell , Jason Yim , Olaf Ronneberger , Sebastian Bodenstein , Michal Zielinski , Alex Bridgland , Anna Potapenko , Andrew Cowie , Kathryn Tunyasuvunakool , Rishub Jain , Ellen Clancy , Pushmeet Kohli , John Jumper , and Demis Hassabis . Protein complex prediction with AlphaFold-Multimer, March 2022. URL https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2. Pages: 2021.10.04.463034 Section: New Results. source
The citation identifies AlphaFold-Multimer as the comparator. The Results (p. 5) describe this paper's AlphaFold-Multimer predictions and benchmarks. The 2,978-complex comparison and 53.2% figure are presented as the citing authors' own results, not findings attributed to this source. (LLM, conf=0.92)
- Supportedcites [39] source
Citation (39) is attached to “DockQ” and identifies the quality measure used for the categorization. The Conclusions describe DockQ as a protein–protein docking model quality score that closely recapitulates the four CAPRI quality classes; the citation is not offered as the source of the ESMFold comparison or its 53.2% result. (LLM, conf=0.93)
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This training also results in the emergence of structure in the models.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here we show that direct inference of structure from primary sequence using a large language model enables an order of magnitude speed-up in high resolution structure prediction.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We discover that an atomic resolution structure prediction can be projected from the representations of the ESM-2 language models.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We are able to complete this characterization in 2 weeks on a heterogeneous cluster of 2,000 GPUs”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in an improvement in speed of up to 60x on the inference forward pass alone”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The atlas reveals more than 225 million high confidence predictions”
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
“High confidence predictions are made for over 225M structures”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
- It may state two separate claims as one. 32
- Probably not a substantial claim. 27
- The quote only partly states this claim. 1
- Borderline: one of the tests for a substantial claim is unclear. 1
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“This is the first large-scale structural characterization of metagenomic proteins, with more than 617 million structures.”
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
“We present the first evolutionary scale structural characterization of a metagenomic resource, folding practically all sequences in MGnify90 (25), over 617M proteins.”
What the sources say
- Could not verifycites [25] sourceExperimental — accuracy not yet measured
The ‘UPDATES TO THE MGnify PROTEIN DATABASE’ section describes more than 1.1 billion protein sequences and their clustering, not predicted structures. Searches for the ESM Metagenomic Atlas, 617 million structures, and protein folding found no passage addressing the claimed structural characterization. (LLM, conf=0.76)
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- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Our second test set consists of 51 targets from the CASP14 competition (37).”
What the sources say
- Partly supportedcites [37] sourceExperimental — accuracy not yet measured
The Introduction describes CASP14 modeling targets, supporting their use as a source for a test set. It reports 68 tertiary-structure modeling targets, but does not identify the citing authors’ 51-target selection or verify its size. (LLM, conf=0.85)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“MGnify sequence MGYP000936678158 has no significant sequence matches to any entry in UniRef90, nor any significant matches via a jackhmmer (47) reference proteome search, but has a predicted structure conserved across many nucleases (PDB 5YET B, TM-score 0.68; PDB 3HR4 A, TM-score 0.67)”
What the sources say
- Supportedcites [47] source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“During method development (e.g. hyperparameter selection), we used a temporally held out validation set obtained from the Continuous Automated Model EvaluatiOn (CAMEO) server (36) by filtering from August 2021 to January 2022.”
What the sources say
- Supportedcites [36] source
The citation identifies the CAMEO platform from which the citing authors obtained data. The Introduction describes CAMEO’s automated evaluations and says its benchmarking results are publicly available for methods development. The August 2021–January 2022 filtering is the citing authors’ own procedure, not a claim attributed to this 2018 source. (LLM, conf=0.92)
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- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The resulting ESM-2 model family significantly outperforms previously state-of-the-art ESM-1b (a ∼ 650 million parameter model) at a comparable number of parameters, and on structure prediction benchmarks it also outperforms other recent protein language models (Table S3).”
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In the paper
“At 270K training steps the 8M parameter model has a perplexity of 10.45, and the 15B model reaches a perplexity of 6.37”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in prediction that is up to 60x faster than state-ofthe-art while maintaining resolution and accuracy.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“This removes costly aspects of current state-of-the-art structure prediction pipelines, eliminating the need for a multiple sequence alignment, while at the same time greatly simplifying the neural architecture used for inference.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“Attention patterns develop in ESM-2 that correspond to tertiary structure (Fig. 1A), and scaling leads to large improvements in the understanding of structure (Fig. 1B).”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“and ∼ 225 million predictions with high confidence (mean pLDDT > 0 . 7 and pTM > 0 . 7 ) corresponding to ∼ 36% of total structures folded (Fig. 3).”
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In the paper
“The first test is from CAMEO, consisting of all 194 test proteins from April 01, 2022 through June 25, 2022.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in a speed improvement for structure prediction of more than an order of magnitude while maintaining high resolution accuracy.”
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In the paper
“We were able to complete the predictions in 2 weeks on a cluster of approximately 2,000 GPUs (Appendix A.4.1).”
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In the paper
“On the CAMEO test set, language model perplexity has a Pearson correlation of -0.55 with the TM-score between the predicted and experimental structures; on CASP14, the correlation is -0.67 (Fig. 2B).”
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In the paper
“This results in 25,450 clusters covering a total of 325,498 chains.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ESMFold achieves competitive accuracy with RosettaFold on CAMEO, which averages a TM-score of 0.82.”
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In the paper
“The average long range P@L was 0.4287 with a standard deviation of 0.0028.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“MGnify sequence MGYP004000959047 has no significant UniRef90 or jackhmmer reference proteome matches but its predicted structure has high similarity to experimental structures of lipid binding domains (PDB 6BYM A, TMscore 0.80; PDB 5YQP B, TM-score 0.78)”
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In the paper
“Using the search protocols released with AlphaFold2, including MSAs and templates, AlphaFold2 achieves 0.88 and 0.85 on CAMEO and CASP14 respectively.”
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In the paper
“For Hsu et al. (62) we generated a set of 13,477,259 structure predictions with AlphaFold2 using MSAs generated via the process in Rao et al. (40).”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“The 15 billion parameter model reaches a TM-score of 0.71 on the CAMEO test set and 0.54 on the CASP14 test set, 0.064 points higher than the 150 million parameter ESM-2 model on both (Fig. 1E).”
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In the paper
“This characterization reveals the structures of hundreds of millions proteins that have been previously unknown, millions of which are novel in comparison to experimentally determined structures.”
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In the paper
“ESMFold achieves an average TM-score of 0.83 on CAMEO and 0.68 on CASP14.”
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In the paper
“No filtering is performed on these test sets, as ESMFold is able to make predictions on all sequences, including the length-2166 target T1044.”
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In the paper
“Because of the way the dataset is constructed, only 1.5% of the dataset is removed with this filter.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Language models greatly accelerate state-of-the-art high resolution structure prediction.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearBorderline substantial claim. It is unclear why the paper says it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On a single NVIDIA V100 GPU, ESMFold makes a prediction on a protein with 384 residues in 14.2 seconds, 6x faster than a single AlphaFold2 model.”
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In the paper
“Across all models (Table S3) there is a correlation of -0.99 between validation perplexity and CASP14 TM-score, and -1.00 between validation perplexity and CAMEO TM-score”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“On the CAMEO test set, ESMFold predictions have a median all-atom RMSD 95 (root-mean-squared deviation at 95% residue coverage) of 1.91 ˚ A and backbone RMSD 95 of 1.33 ˚ A.”
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In the paper
“For RoseTTAfold, the speed of the SE(3) Transformer dominates, especially at low sequence lengths.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“These steps result in a total of 2978 clusters.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“At low sequence lengths, ESMFold is dominated by language model performance, while the O ( N 3 ) computation of pairwise representations takes over at high sequence lengths.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“Differentiation is greater in the ªmediumº and ªhardº classes, which have lower MSA depth and are better predicted by AlphaFold2.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“We see an over 60x speed advantage for shorter protein sequences, and a reasonable speed advantage for longer protein sequences.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which we were able to do in 2 weeks time.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Unsupervised contact prediction results show that both RoPE and newer data significantly improve the results.”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
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In the paper
“We estimate that on a homogeneous network GPU cluster of V100s, the entire 620 million sequences would take approximately 28,000 GPU days to fold”
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“Language model perplexity and ESMFold pLDDT are both well correlated with actual structure prediction accuracy on CASP14 and CAMEO.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After 270K updates the 8M parameter model has a perplexity of 10.45, and the 15B model reaches a perplexity of 6.37.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After filtering, there are 104 sequences with MSA depth ≤ 100 , 70 sequences with MSA depth ≤ 10 , and 22 sequences with MSA depth = 1.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“analyzed the high-confidence subset with mean pLDDT greater than 0.9, corresponding to ∼ 59K structures.”
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This results in a total of 3505 unique chain pairs.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We do observe a slight regression when sampling from UniRef90 clusters”
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These capabilities develop as a function of scale, with greater capabilities emerging as computation, data, and number of parameters increase.”
What the sources say
- Not matchedcites 19±22
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Within a protein family, structure and function can be inferred from the patterns in sequences (4, 5).”
What the sources say
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that improvement on sequences with high evolutionary depth saturates at lower model scales, and improvement on sequences with low evolutionary depth continues as models increase in size.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“language models of text, despite the simplicity of their training objectives, such as filling in missing words or predicting the next word, are shown to develop emergent capabilities that are connected to the underlying meaning of the text.”
What the sources say
- Not matchedcites 19±22
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the language models are scaled they learn information that enables prediction of the three-dimensional structure of a protein at the resolution of individual atoms.”
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
“We discover that atomic resolution structure prediction emerges and continues to improve in language models over the four orders of magnitude in parameter scale.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § Evolutionary-scale prediction of atomic level protein structure with a language model · p. 2 · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“No filtering is performed on these test sets, as ESMFold is able to make predictions on all sequences, including the length-2166 target T1044.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All train sequences which match a validation sequence with 50% sequence identity under this search are removed from the train set.”
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We report results by testing 3D structure prediction models on two test sets, both chosen to be temporally held out from our supervised training set.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All compared sequences are temporally held out from the training set.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“however we believe this difference is small and the UniRef90 cluster sampling is likely to help for the larger models.”
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The number of SE(3) max-iterations are artificially limited to 20 (default 200) and no MSAs are used as input for these measurements.”
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, this only measures the network forward time, and does not include the time taken to compute sidechains with PyRosetta or search for MSAs.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Since BERT style models have been shown to be amenable to very large batch sizes (67), we increased our effective batch size to 2M tokens.”
What the sources say
- Not matchedcites [67] Yang You , Jing Li , Sashank Reddi , Jonathan Hseu , Sanjiv Kumar , Srinadh Bhojanapalli , Xiaodan Song , James Demmel , Kurt Keutzer , and Cho-Jui Hsieh. Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes. page 38, 2020.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“even though ESMFold is not trained on protein complexes.”
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper relies on this without support.
AlphaFold2 predictions provide a valid reference for assessing the accuracy of ESMFold predictions on metagenomic proteins.
“We also assess calibration against AlphaFold predictions on metagenomic proteins.”
“On a random subset of ∼ 4K metagenomic sequences, there is a high correlation (Pearson r = 0.79) between ESMFold pLDDT and the LDDT to AlphaFold2 predictions (Fig. 3A).”
The paper relies on this without support.
The MGnify90 database is representative of metagenomic protein diversity.
“We fold over 617 million sequences from the MGnify90 database (25).”
The paper relies on this without support.
The PDB database provides a comprehensive reference for experimentally determined protein structures.
“no structure is found with TM-score over 0.5 in the PDB database”
“no experimentally determined structure with a similar fold could be identified”
The paper relies on this without support.
Absolute positional encoding methods don't extrapolate well beyond the context window they are trained on.
“However, absolute positional encoding methods don't extrapolate well beyond the context window they are trained on.”
The paper relies on this without support.
Orphan proteins are very challenging for current structure prediction models due to a lack of evolutionary information.
“Due to a lack of evolutionary information, these sequences can be very challenging for current structure prediction models.”
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?
The cited source says what the claim attributes to it.
In the citing paper
“Evo also reaches competitive performance with leading protein-specific language models (41, 46–48) (Fig. 2B).”
From Sequence modeling and design from molecular to genome scale with Evo (2024) · cites this paper as [47]
The cited source says what the claim attributes to it.
In the citing paper
“Such modeling paradigms have been used successfully in the biomolecular realm, for example, to predict three-dimensional (3D) molecular structures from sequences32–34 and interactions between different molecular components.35–38”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [34]
In the citing paper
“are the best and most accurate answer to the structure prediction problem to date”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [19]
In the citing paper
“The unprecedented accuracy of AlphaFold2,127 RosettaFold,128 and others129,130 offers, at least in principle, the opportunity to virtually screen any protein of interest with the structure-based approach, irrespective of its a priori ligandability (after AlphaFold2, the structural coverage of the human proteome increased from 48% to 76% of the total residues).131”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [129]
In the citing paper
“This difficulty is amplified by the fact that the repository of publicly available structures, consisting of the combined databases of AFDB and the ESMatlas38, is approaching a billion entries.”
From Clustering predicted structures at the scale of the known protein universe (2023) · cites this paper as [38]
In the citing paper
“the ESM Atlas contains over 617 million metagenomic structures predicted by ESMFold4.”
From Fast and accurate protein structure search with Foldseek (2024) · cites this paper as [4]
In the citing paper
“Our current proof-of-concept model is considerably smaller—by 200 times—than the largest published protein language model (49).”
From DNA language models are powerful predictors of genome-wide variant effects (2023) · cites this paper as [49]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Conflicting PDB identifier for the nuclease example
MinorThe main text identifies the comparison structure as PDB 3HR4 A, but the Figure 4 caption identifies it as PDB 3H4R. These are different identifiers for the same described example.
“PDB 3HR4 A, TM-score 0.67”
“The predicted structure of MGYP000936678158 aligns to an experimental structure from a bacterial nuclease (light brown, PDB: 3H4R)”
Citations
Cites
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012) · Science · doi:10.1126/science.1225829 · Cited by 17,116
ColabFold - Making protein folding accessible to all
Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., & Steinegger, M. (2021) · doi:10.1101/2021.08.15.456425 · Cited by 9,391
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1061 · Cited by 7,850
Flagged first, then by citations
Research works printed but not linked: 1
- Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , Łukasz Kaiser , and Illia Polosukhin . Attention Is All You Need. In Advances in Neural Information Processing Systems, pages 5998–6008, 2017. URL https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf.
Cited by
Sorted by citations
Items 401–500 of 5,663
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Ekambaram, S., & Dokholyan, N. V. (2025) · Chemical Communications · doi:10.1039/d5cc04998a · Cited by 53
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Ding, K., Yu, J., Huang, J., Yang, Y., Zhang, Q., & Chen, H. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00849-y · Cited by 73
Livesey, B. J., & Marsh, J. A. (2025) · Genome Biology · doi:10.1186/s13059-025-03575-w · Cited by 50
HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes
Giulini, M., Reys, V., Teixeira, J. M. C., Jiménez-García, B., V. Honorato, R., Kravchenko, A., Xu, X., Versini, R., Engel, A., Verhoeven, S., et al. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00969 · Cited by 51
Nielsen, H. (2025) · Methods in molecular biology · doi:10.1007/978-1-0716-4623-6_10 · Cited by 48
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Burbach, S. M., & Briney, B. S. (2024) · Patterns · doi:10.1016/j.patter.2024.100967 · Cited by 40
Waight, A. B., Příhoda, D., Shrestha, R., Metcalf, K. J., Bailly, M. A., Ancona, M., Widatalla, T., Rollins, Z. A., Cheng, A. C., Bitton, D. A., et al. (2023) · mAbs · doi:10.1080/19420862.2023.2248671 · Cited by 38
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Chen, C., Wang, H., Chen, Y., Yin, Z., Yang, X., Ning, H., Zhang, Q., Li, W., Xiao, R., & Zhao, J. (2023) · Brain‐X · doi:10.1002/brx2.29 · Cited by 44
McMaster, B., Thorpe, C. J., Ogg, G. S., Deane, C. M., & Koohy, H. (2024) · Nature Methods · doi:10.1038/s41592-024-02240-7 · Cited by 42
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Yuan, Q., Tian, C., Song, Y., Ou, P., Zhu, M., Zhao, H., & Yang, Y. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae381 · Cited by 41
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Concha-Eloko, R., Stock, M., Bernard De Baets, C., Briers, Y., Sanjuán, R., Domingo‐Calap, P., & Boeckaerts, D. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011831 · Cited by 41
Buller, R., Damborský, J., Hilvert, D., & Bornscheuer, U. T. (2024) · Angewandte Chemie International Edition · doi:10.1002/anie.202421686 · Cited by 42
Ni, B., Kaplan, D. L., & Buehler, M. J. (2024) · Science Advances · doi:10.1126/sciadv.adl4000 · Cited by 51
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Pallikkavaliyaveetil, N., & Chandrasekaran, S. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115762 · Cited by 57
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Fesenko, I. A., Sahakyan, H., Dhyani, R., Shabalina, S. A., Storz, G., & Koonin, E. V. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.01.025 · Cited by 41
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New strategies to enhance the efficiency and precision of drug discovery
An, Q., Huang, L., Wang, C., Wang, D., & Tu, Y. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1550158 · Cited by 45
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Zhu, H., & Ding, Y. (2025) · Biology · doi:10.3390/biology14050547 · Cited by 44
Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering
Chen, J., Singh, N., Lu, J., Lane, S., & Zhao, H. (2025) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2025.103380 · Cited by 42
Nitrogenase resurrection and the evolution of a singular enzymatic mechanism
Garcia, A. K., Harris, D. F., Rivier, A. J., Carruthers, B. M., Pinochet-Barros, A., Seefeldt, L. C., & Kaçar, B. (2023) · eLife · doi:10.7554/elife.85003 · Cited by 35
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Zhong, B., Zheng, L., Wu, B., Tan, Y., Lv, O., Yi, K., Fan, G., & Hong, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00036 · Cited by 39
Xu, Y., Liu, D., & Gong, H. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00716-2 · Cited by 41
Wohlwend, J., Nathan, A., Shalon, N., Crain, C. R., Tano-Menka, R., Goldberg, B., Richards, E., Gaiha, G. D., & Barzilay, R. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00971-y · Cited by 41
Liu, J., Wu, L., Xie, A., Liu, W. L., Zhao, H., Wan, Y., & Mao, W. (2025) · Journal of Nanobiotechnology · doi:10.1186/s12951-025-03169-5 · Cited by 42
Lai, Q., Yao, S., Zha, Y., Zhang, H.-H., Zhang, H.-B., Ye, Y., Zhang, Y.-H., Bai, H., & Ning, K. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf305 · Cited by 28
Polonsky, K., Pupko, T., & Freund, N. T. (2023) · The Journal of Immunology · doi:10.4049/jimmunol.2300150 · Cited by 41
Protein representations: Encoding biological information for machine learning in biocatalysis
Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38
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Alrashed, S., & Min‐Allah, N. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101606 · Cited by 40
Lin, A., Ye, J., Qi, C., Zhu, L., Mou, W., Gan, W., Zeng, D., Tang, B., Xiao, M., Chu, G., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf357 · Cited by 43
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Wu, S., Zhang, S., Liu, C., Fernie, A. R., & Yan, S. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100887 · Cited by 43
Jahanshahi, D. A., Barzani, M. R. R., Bahram, M., Ariaeenejad, S., & Kavousi, K. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117640 · Cited by 41
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Xu, S. Y., Feng, Q., Qiao, L., Wu, H., Shen, T., Cheng, Y., Zheng, S., & Sun, S. (2025) · Nature Communications · doi:10.1038/s41467-025-67127-3 · Cited by 73
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Jin, S., Zeng, Z., Xiong, X., Huang, B., Tang, L., Wang, H., Ma, X., Tang, X., Shao, G., Huang, X., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08282-7 · Cited by 43
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Emonts, J., & Buyel, J. F. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.05.022 · Cited by 37
Li, G., Yao, S., & Fan, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01697 · Cited by 37
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Wang, C., Li, M., He, J., Wang, Z., Darzidehkalani, E., Chen, Z., Ye, J., Li, T., Su, Y., Ke, J., et al. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103268 · Cited by 65
Peptide binder design with inverse folding and protein structure prediction
Bryant, P., & Elofsson, A. (2023) · Communications Chemistry · doi:10.1038/s42004-023-01029-7 · Cited by 38
ABodyBuilder3: improved and scalable antibody structure predictions
Kenlay, H., Dreyer, F. A., Cutting, D., Nissley, D. A., & Deane, C. M. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae576 · Cited by 44
Deep Learning for Antimicrobial Peptides: Computational Models and Databases
Zhou, X., Liu, G., Cao, S., & Lv, J. Z. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00006 · Cited by 37
Wang, Y., Lv, H., Teo, Q. W., Lei, R., Gopal, A. B., Ouyang, W. O., Yeung, Y.-H., Tan, T. J. C., Choi, D., Shen, I. R., et al. (2024) · Immunity · doi:10.1016/j.immuni.2024.07.022 · Cited by 35
Generative latent diffusion language modeling yields anti-infective synthetic peptides
Torres, M. D. T., Chen, T., Wan, F., Chatterjee, P., & de la Fuente‐Núñez, C. (2025) · Cell Biomaterials · doi:10.1016/j.celbio.2025.100183 · Cited by 43
Wang, J., Watson, J. L., & Lisanza, S. L. (2024) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a041472 · Cited by 43
Challenges in bridging the gap between protein structure prediction and functional interpretation
Varadi, M., Tsenkov, M., & Velankar, S. (2025) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26614 · Cited by 36
Segment Any Cell: A SAM-Based Auto-Prompting Fine-Tuning Framework for Nuclei Segmentation
Na, S., Guo, Y., Jiang, F., Ma, H., Gao, J., & Huang, J. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3611322 · Cited by 36
The transformative power of transformers in protein structure prediction
Moussad, B., Roche, R., & Bhattacharya, D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303499120 · Cited by 37
Protein stability prediction by fine-tuning a protein language model on a mega-scale dataset
Chu, S. K. S., Narang, K., & Siegel, J. B. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012248 · Cited by 39
Interpreting the molecular mechanisms of disease variants in human transmembrane proteins
Tiemann, J. K. S., Zschach, H., Lindorff-Larsen, K., & Stein, A. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2022.12.031 · Cited by 35
Artificial intelligence for neurodegenerative experimental models
Marzi, S. J., Schilder, B. M., Nott, A., Frigerio, C. S., Willaime‐Morawek, S., Bucholc, M., Hanger, D. P., James, C. R., Lewis, P. A., Lourida, I., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13479 · Cited by 35
Gong, J., Jiang, L., Chen, Y., Zhang, Y., Li, X., Ma, Z., Fu, Z., He, F., Sun, P., Ren, Z., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad646 · Cited by 37
PROSTATA: a framework for protein stability assessment using transformers
Umerenkov, D., Nikolaev, F., Shashkova, T., Страшнов, П. В., Sindeeva, M., Shevtsov, A., Ivanisenko, N. V., & Kardymon, O. L. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad671 · Cited by 40
Jannati, S., Patnaik, R., & Banerjee, Y. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25168727 · Cited by 43
Aligning protein generative models with experimental fitness via Direct Preference OptimizationPreprint
Widatalla, T., Rafailov, R., & Hie, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.20.595026 · Cited by 35
Self-citationAdvancing structural biology through breakthroughs in AI
Aithani, L., Alcaide, E., Bartunov, S., Cooper, C. D. O., Dore, A. S., Lane, T. J., Maclean, F., Rucktooa, P., Shaw, R. A., & Skerratt, S. E. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102601 · Cited by 35
Equivariant Flexible Modeling of the Protein–Ligand Binding Pose with Geometric Deep Learning
Dong, T., Yang, Z., Zhou, J., & Chen, C. Y. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00273 · Cited by 36
Zhang, X. J., Hu, X., Zhang, T., Yang, L., Liu, C., Xu, N., Wang, H., & Sun, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae404 · Cited by 38
Generative AI: driving productivity and scientific breakthroughs in pharmaceutical R&D
Doron, G., Genway, S., Roberts, M., & Jasti, S. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104272 · Cited by 39
Structure‐aware deep learning model for peptide toxicity prediction
Ebrahimikondori, H., Sutherland, D., Yanai, A., Richter, A., Salehi, A., Li, C., Coombe, L., Kotkoff, M., Warren, R. L., & Birol, İ. (2024) · Protein Science · doi:10.1002/pro.5076 · Cited by 43
Grigorakis, K., Ferousi, C., & Topakas, E. (2025) · Catalysts · doi:10.3390/catal15020147 · Cited by 39
Rapid directed evolution guided by protein language models and epistatic interactions
Tran, V. Q., Nemeth, M., Bartie, L. J., Chandrasekaran, S. S., Fanton, A., Moon, H. C., Hie, B., Konermann, S., & Hsu, P. D. (2026) · Science · doi:10.1126/science.aea1820 · Cited by 41
Self-citationWang, D., Pourmirzaei, M., Abbas, U. L., Zeng, S., Manshour, N., Esmaili, F., Poudel, B., Jiang, Y., Shao, Q., Chen, J., et al. (2025) · Advanced Science · doi:10.1002/advs.202404212 · Cited by 46
Flexible protein–protein docking with a multitrack iterative transformer
Chu, L., Ruffolo, J. A., Harmalkar, A., & Gray, J. J. (2024) · Protein Science · doi:10.1002/pro.4862 · Cited by 41
Roche, R., Moussad, B., Shuvo, M. H., & Bhattacharya, D. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011435 · Cited by 35
Boulos, I., Jabbour, J., Khoury, S., Mikhael, N., Tishkova, V., Candoni, N., Ghadieh, H. E., Veesler, S., Bassim, Y., Azar, S. T., et al. (2023) · Molecules · doi:10.3390/molecules28207176 · Cited by 38
A Perspective on the Prospective Use of AI in Protein Structure Prediction
Versini, R., Sritharan, S., Fas, B. A., Tubiana, T., Aimeur, S., Henri, J., Erard, M., Nüße, O., Andréani, J., Baaden, M., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01361 · Cited by 34
RNAformer: A Simple yet Effective Model for Homology-Aware RNA Secondary Structure PredictionPreprint
Franke, J. K. H., Runge, F., Koeksal, R., Matus, D., Backofen, R., & Hutter, F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.12.579881 · Cited by 35
Hu, J., Li, Z., Rao, B., Thafar, M. A., & Arif, M. (2024) · Analytical Biochemistry · doi:10.1016/j.ab.2024.115550 · Cited by 34
Mullin, M. J., McClory, J., Haynes, W. A., Grace, J., Robertson, N. J., & Heeke, G. V. (2024) · mAbs · doi:10.1080/19420862.2024.2341443 · Cited by 36
Accurate prediction of antibody function and structure using bio-inspired antibody language model
Jing, H., Gao, Z., Xu, S. Y., Shen, T., Peng, Z., He, S., You, T., Ye, S., Lin, W. J., & Sun, S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae245 · Cited by 35
ProSST: Protein Language Modeling with Quantized Structure and Disentangled AttentionPreprint
Li, M., Tan, P., Ma, X., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.15.589672 · Cited by 34
AI approaches for the discovery and validation of drug targets
Wenteler, A., Cabrera, C., Wei, W., Neduva, V., & Barnes, M. R. (2024) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2024.4 · Cited by 38
Enriching productive mutational paths accelerates enzyme evolution
Patsch, D., Schwander, T., Voß, M., Schaub, D., Hüppi, S., Eichenberger, M., Stockinger, P., Schelbert, L., Giger, S., Peccati, F., et al. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01712-3 · Cited by 37
Aranganathan, A., Gu, X., Wang, D., Vani, B. P., & Tiwary, P. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103000 · Cited by 39
Tokenization in the Theory of Knowledge
Friedman, R. (2023) · Encyclopedia · doi:10.3390/encyclopedia3010024 · Cited by 41
Son, A., Kim, W., Park, J., Lee, W., Lee, Y., Choi, S., & Kim, H. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25179725 · Cited by 39
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 38
AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors
Fernández-Díaz, R., Cossio-Pérez, R., Agoni, C., Lam, H. T., López, V., & Shields, D. C. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae555 · Cited by 35
PlasmidScope: a comprehensive plasmid database with rich annotations and online analytical tools
Li, Y., Feng, X., Chen, X., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae930 · Cited by 34
Representing structures of the multiple conformational states of proteins
Ramelot, T. A., Tejero, R., & Montelione, G. T. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102703 · Cited by 26
Giulini, M., Schneider, C., Cutting, D., Desai, N., Deane, C. M., & Bonvin, A. M. J. J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae583 · Cited by 41
Akpinaroglu, D., Seki, K., Guo, A., Zhu, E., Kelly, M. J. S., & Kortemme, T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.15.571823 · Cited by 36
Recent Advances and Challenges in Protein Structure Prediction
Peng, C., Liang, F., Xia, Y., Zhao, K., Hou, M., & Zhang, G. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01324 · Cited by 42
Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning
Vornholt, T., Mutný, M., Schmidt, G. W., Schellhaas, C., Tachibana, R., Panke, S., Ward, T. R., Krause, A., & Jeschek, M. (2024) · ACS Central Science · doi:10.1021/acscentsci.4c00258 · Cited by 36
Vaccine development using artificial intelligence and machine learning: A review
Asediya, V., Anjaria, P. A., Mathakiya, R. A., KORINGA, P. G., Nayak, J. B., Bisht, D., Fulmali, D., Patel, V. A., & Desai, D. N. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.136643 · Cited by 35
Orand, T., & Jensen, M. R. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102958 · Cited by 34
Metadata
- Authors
- Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., & Rives, A.
- Affiliations
- New York University (US)
- Stanford University (US)
- Corresponding author
- Rives, Alexander
- ORCID
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2022-07-21
- Volume
- 379
- Issue
- 6637
- Pages
- 1123-1130
- ISSN
- 0036-8075 (ISSN-L)
- OpenAlex
- W4327550249
- Cited by
- 5,990
- References
- 71
- Citations by year
- 2026: 2468 · 2025: 1837 · 2024: 1170 · 2023: 485 · 2022: 23
- FWCI
- 460.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein structure prediction, large language models, protein evolution, atomic-scale structure, protein folding, structural diversity
- MeSH terms
- Machine Learning, Amino Acid Sequence, Protein Conformation, Proteins, Evolution, Molecular, Sequence Analysis, Protein
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.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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 ›
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