The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index; 2 of the 23 sources its claims cite were checked for support.
Large language models generate functional protein sequences across diverse families
Open accessAbstract
Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.
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32 Own result · 14 Own interpretation · 11 Not checked yet · 8 Own method · 4 No basis given · 1 Couldn’t confirm · accuracy not yet measured
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In the paper
“This indicates that the model has captured evolutionary conservation patterns without training on explicit alignment information such as with Potts models51, as implemented in direct coupling analysis7,52–55.”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Not checked yetcites [51] Mchaourab, H. S., Oh, K. J., Fang, C. J. & Hubbell, W. L. Conformation of T4 lysozyme in solution. Hinge-bending motion and the substrate-induced conformational transition studied by site-directed spin labeling. Biochemistry 36, 307–316 (1997). source
- Not checked yetcites [7] Russ, W. P. et al. An evolution-based model for designing chorismate mutase enzymes. Science 369, 440–445 (2020). source
- Could not verifycites [52] Kim, J.-K. et al. BetaCavityWeb: a webserver for molecular voids and channels. Nucleic Acids Res. 43, W413–W418 (2015). sourceExperimental — accuracy not yet measured
The Methods and Materials passages describe Voronoi-based identification of molecular voids and channels, not a model learning evolutionary conservation. Searches for alignment information, Potts models, and direct coupling analysis found no passage in our copy addressing the attributed claim. (LLM, conf=0.78)
- Not checked yetcites [53] Rost, B. Twilight zone of protein sequence alignments. Protein Eng. 12, 85–94 (1999). source
- Not checked yetcites [54] Pearson, W. R. An introduction to sequence similarity (‘homology’) searching. Curr. Protoc. Bioinforma. 3, 3.1 (2013). ChapterUnit.
- Not checked yetcites [55] Repecka, D. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3, 324–333 (2021). source
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In the paper
“As in the crystal structure of L056, the predicted artificial structures roughly match known structures found in nature, Fig S11, including for low identity (<40%) artificial sequences.”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Could not verifycites [14] Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). sourceExperimental — accuracy not yet measured
The Abstract and Main section describe AlphaFold’s structure-prediction accuracy, including cases without a known similar structure. Our copy does not address whether predicted structures of functional artificial sequences resemble natural structures, including at below 40% sequence identity (searched: artificial sequences, L056, and 40% identity). (LLM, conf=0.77)
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“Our protein language model is trained by simply learning to predict the next amino acid for over 280 million protein sequences from thousands of protein families, without explicit biophysical modeling.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“After training, ProGen can be prompted to generate full-length protein sequences for any protein family from scratch, with a varying degree of similarity to natural proteins.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“Our artificial lysozymes span the sequence landscape of natural lysozymes, Fig. 2a, across five families which contain diverse protein folds, active site architectures, and enzymatic mechanisms49,50.”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Not checked yetcites [49] Baase, W. A., Liu, L., Tronrud, D. E. & Matthews, B. W. Lessons from the lysozyme of phage T4. Protein Sci. 19, 631–641 (2010). source
- Not checked yetcites [50] Kuroki, R., Weaver, L. H. & Matthews, B. W. A covalent enzyme–substrate intermediate with saccharide distortion in a mutant T4 lysozyme. Science 262, 2030–2033 (1993). source
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- Rests on an assumption: The five lysozyme families chosen because they lack disulfide bonds can be expressed in high-throughput cell-free systems.
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In the paper
“they demonstrate similar residue position entropies when forming separate multiple sequence alignments of natural and artificial proteins within each family, Fig. 2c.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“In the positive control set of 100 natural proteins, 72% were well-expressed based on chromatography peaks and band visualization.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“Our artificial proteins match activity levels of natural proteins even at lower levels of sequence identity to any known natural protein, (Fig 3b, Fig. S5).”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“The representative natural proteins exhibited similar levels of functionality with 59% (53/90) of total proteins considered functional.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“None of the bmDCA artificial proteins exhibited a detectable level of functionality, Fig. S10”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Not checked yetcites [7] Russ, W. P. et al. An evolution-based model for designing chorismate mutase enzymes. Science 369, 440–445 (2020). source
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- Probably not a substantial claim. 16
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In the paper
“ProGen is a 1.2 billion-parameter neural network trained using a publicly available dataset of 280 million protein sequences.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“we could readily measure the KM values for the purified apparent lower molecular weight species, where both L056 and L070 were highly active and had comparable Michaelis-Menten parameters to HEWL (Fig. 3e).”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“Among our artificial proteins, 73% (66/90) were functional and exhibited high levels of functionality across families, Fig. 3c.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“Notably a small number of proteins, both within the natural and artificial proteins, were within an order of magnitude of HEWL, which was substantially more active than all negative controls.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“Two proteins, L056 (max ID 69.6%) and L070 (max ID 89.2%) expressed well and incurred bactericidal activities towards the E. coli BL21(DE3) strain used during overnight induction at 16C.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“The ProGen-generated proteins express equally well (72/100 total) across all bins of sequence identity to any known natural protein (maxID 40–90%), Fig 2e.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“Despite having comparable enzymatic activities, L070 and L056 only share 17.9% sequence ID.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“We also found that there is no bias to location or structural element to the mutations that diverge L056 and L070 from their respective nearest sequence homolog in nature. Differing residues are instead uniformly distributed.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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In the paper
“The KM values of both monomers were too weak to be measured using a heterogeneous, fluorescein-labeled Micrococcus lysodeikticus cell wall substrate (Molecular Probes EnzChek Lysozyme kit); however, both monomers were active using a pseudo-first order kinetic assay (Fig. S7).”
§ Large language models generate functional protein sequences across diverse families > Abstract
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In the paper
“Some mutations are even found within the active site cleft and within regions that influence conformational state (for L056).”
§ Large language models generate functional protein sequences across diverse families > Abstract
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In the paper
“We determined a 2.5 Å resolution crystal of L056 (see Methods, Figure 3e). The global fold was similar to predictions, with a Cα RMSD of 2.9 Å from the backbone structure predicted by trRosetta and 2.3 Å RMSD from a WT T4 Lysozyme structure59,60.”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
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In the paper
“bmDCA was unable to fit three out of the five lysozyme families, and exhibited 60% detectable expression (30/50 proteins) for the remaining two protein families.”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Not checked yetcites [7] Russ, W. P. et al. An evolution-based model for designing chorismate mutase enzymes. Science 369, 440–445 (2020). source
plainn = 5- Asserted: passes — The authors put it forward as true.
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In the paper
“Alignment with a structure featuring a covalently trapped substrate (PDB 148L) reveals that the active site cleft is well formed with the key catalytic residue Glu15 (Glu 11 in T4L) and key substrate binding residue Thr30 (Thr26 in T4L) correctly positioned.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“These likelihoods showed a significant drop in AUC of 0.18 for CM (p<0.0001, two-tailed test, n=1617) and 0.08 for MDH (p<0.1, two-tailed test, n=56), as compared to fine-tuned ProGen when predicting if an artificial sequence should function.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“On MDH function data, ProGen log-likelihoods had an AUC of 0.94 (Fig. 4f), which was better than ProteinGAN discriminator scores, with an AUC of 0.87 (p<0.1, two-tailed test, n=56).”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Not checked yetcites [66] Vig, J. et al. BERTology meets biology: Interpreting attention in protein language models. In International Conference on Learning Representations (ICLR, 2020). source
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“On CM function data, ProGen log-likelihoods had an area under the curve (AUC) of 0.85, significantly better (p<0.0001, two-tailed test, n=1617) than bmDCA, which had an AUC of 0.78 (Fig. 4c).”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Not checked yetcites [7] Russ, W. P. et al. An evolution-based model for designing chorismate mutase enzymes. Science 369, 440–445 (2020). source
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In the paper
“We purified 6 highly expressed proteins and found that they were all active, but with significantly lower Michaelis-Menten activities than HEWL or the previously generated artificial proteins L056 and L070 (Fig 3f).”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“Of the selected sequences, 78 out of the 89 (88%) expressed well and 24 out of the 78 (31%) were soluble (Supplemental Figure 8).”
§ Large language models generate functional protein sequences across diverse families > Abstract
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“L013 and L038 expressed highly to inclusion bodies.”
§ Large language models generate functional protein sequences across diverse families > Methods > E. coli Expression of Lysozyme Variants
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“For lowID lysozyme A5 the above protocol was altered slightly to accommodate lower catalytic activity of these variants: reaction volumes were increased to 20 μL, plate was covered with an optically transparent seal (Microseal ® ‘B’ seal; BioRad) to mitigate sample evaporation, fluorescent reads were taken every 5 mins for 16 hours with 5 s of linear plate shaking prior to each measurement to min”
§ Large language models generate functional protein sequences across diverse families > Methods > Michaelis-Menten kinetics of lysozyme variants using fluorescein-labeled M. lysodeikticus cell wall
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In the paper
“The MDH functional labels are binary, so no thresholding was needed to compute AUCs.”
§ Large language models generate functional protein sequences across diverse families > Methods > Evaluating ProGen on Other Protein Systems
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“The resultant data were not described by a single exponential model but were described by a double exponential model (Eq. 6), likely due to the heterogeneity of the substrate, and all data were fit in IgorPro7.”
§ Large language models generate functional protein sequences across diverse families > Methods > Lysozyme kcat/KM extrapolation from pseudo first-order kinetic data
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“For induced cultures of L056 and L070, significant turbidity was observed in the spent media after cells were pelleted at 3,500 r.c.f. for 30 mins at 4°C.”
§ Large language models generate functional protein sequences across diverse families > Methods > E. coli Expression of Lysozyme Variants
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In the paper
“Two peaks were resolved for each variant and that harbored lysozyme activity against the fluorescein-labeled M. Lysodeikticus cell wall substrate (EnzChek kit; ThermoFisher).”
- Also stated in: § Purification of L056 and L070 from spent media · plain
“Two peaks were resolved for each variant and that harbored lysozyme activity against the fluorescein-labeled M. Lysodeikticus cell wall substrate (EnzChek kit; ThermoFisher) that corresponded to peaks observed in the first pool purification.”
§ Large language models generate functional protein sequences across diverse families > Methods > Purification of L056 and L070 from spent media
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In the paper
“Significant translational non-crystallographic symmetry and differences with the search model resulted in maps that were initially hard to interpret.”
§ Large language models generate functional protein sequences across diverse families > Methods > Crystallization and structure determination of L056
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In the paper
“These results indicate that ProGen generates protein sequences that not only can express well but also maintain enzymatic function for diverse sequence landscapes across protein families.”
§ Large language models generate functional protein sequences across diverse families > Abstract
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- Rests on an assumption: Well-expression and solubility in the cell-free system indicate that the artificial proteins are structurally well-folded.
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In the paper
“Taken together, L056 and L070 harbor potent catalytic activity and bactericidal capabilities that are comparable to HEWL, while diverging from their nearest known natural sequence by 53 and 18 amino acids respectively.”
§ Large language models generate functional protein sequences across diverse families > Abstract
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: HEWL is a suitable reference for native lysozyme catalytic activity.
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“The perplexity on a held-out test set was monitored and did not exceed training set perplexity throughout model training.”
§ Large language models generate functional protein sequences across diverse families > Methods > ProGen Training
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In the paper
“While we do not expect our language model to generate proteins that belong to a completely different distribution or domain (for example creating a new fold that catalyzes an unnatural reaction), it can significantly expand the space of protein sequences from those sampled by evolution.”
§ Large language models generate functional protein sequences across diverse families > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Rests on an assumption: The 280 million-sequence training set is diverse enough to learn a universal protein representation transferable across families.
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 aggregated dataset was split into a training set of size 280 million and two test sets, an out-of-distribution test set (OOD-test) of size 100k from 20 protein families and a randomly sampled in-domain test set (ID-test) of size 1 million, that were held-out for training and used for evaluation.”
§ Large language models generate functional protein sequences across diverse families > Methods > Training Data Curation
plainn = 20- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In sum, ProGen’s model likelihoods are better aligned with experimentally-measured assay data on two diverse protein datasets—chorismate mutase and malate dehydrogenase—than the sequence generation methods from original studies specifically tailored for these families.”
§ Large language models generate functional protein sequences across diverse families > Abstract
What the sources say
- Not checked yetcites [7] Russ, W. P. et al. An evolution-based model for designing chorismate mutase enzymes. Science 369, 440–445 (2020). source
- Not checked yetcites [66] Vig, J. et al. BERTology meets biology: Interpreting attention in protein language models. In International Conference on Learning Representations (ICLR, 2020). source
plaincorrelationaln = 1,617- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It 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
“These results indicate that both components of our training strategy—initial training on the universal sequence dataset and fine-tuning on the protein family of interest—contribute significantly to final model performance.”
§ Large language models generate functional protein sequences across diverse families > Abstract
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“For robustness, we trained three discriminators using different random seeds.”
§ Large language models generate functional protein sequences across diverse families > Methods > Lysozyme Sequence Selection
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Proteins that generated fluorescence one standard deviation above the maximum fluorescence of any negative control were considered functional.”
§ Large language models generate functional protein sequences across diverse families > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“The resultant data were not described by a single exponential model but were described by a double exponential model (Eq. 6), likely due to the heterogeneity of the substrate, and all data were fit in IgorPro7.”
§ Large language models generate functional protein sequences across diverse families > Methods > Lysozyme kcat/KM extrapolation from pseudo first-order kinetic data
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The average molecular weight of the fluorescein-labeled M. lysodeikticus cell wall substrate is unknown and the substrate is heterogeneous.
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 also ensured that no two generated samples share over 80% identity with each other.”
§ Large language models generate functional protein sequences across diverse families > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“After measuring the AUC of each model for each dataset, we used bootstrapping to compute the statistical significance of the difference in AUC of fine-tuned ProGen vs. the reference method (bmDCA and ProGen ablations for CM, ProteinGAN and ProGen ablations for MDH).”
§ Large language models generate functional protein sequences across diverse families > Methods > Evaluating ProGen on Other Protein Systems
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably 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
“The fine-tuning dataset was clustered at 80% sequence identity and 10% of the clusters were held-out as a validation set for hyper-parameter optimization and stopping criteria.”
§ Large language models generate functional protein sequences across diverse families > Methods > Lysozyme Generation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“We also applied thresholding at 0.42 norm relative enrichment (r.e.) to obtain binary labels for CM function, which roughly corresponds to the cut-off point between two modes that exist in CM function data, to be used for ROC curves, following the original study7.”
§ Large language models generate functional protein sequences across diverse families > Methods > Evaluating ProGen on Other Protein Systems
What the sources say
- Not checked yetcites [7] Russ, W. P. et al. An evolution-based model for designing chorismate mutase enzymes. Science 369, 440–445 (2020). source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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 performed structure prediction without an MSA as input so as to not heavily bias the structure prediction toward a known natural mode.”
§ Large language models generate functional protein sequences across diverse families > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
The five lysozyme families chosen because they lack disulfide bonds can be expressed in high-throughput cell-free systems.
“Within the lysozyme clan, we chose to work with five families that lack disulfide bonds as to be amenable to high-throughput cell-free expression: Phage lysozyme, Pesticin, Glucosaminidase, Glycoside hydrolase, and Transglycosylase.”
The paper relies on this without support.
Well-expression and solubility in the cell-free system indicate that the artificial proteins are structurally well-folded.
“These results indicate that ProGen can generate artificial proteins that are structurally well-folded for proper expression as compared to a batch of natural proteins”
The paper relies on this without support.
The 280 million-sequence training set is diverse enough to learn a universal protein representation transferable across families.
“Trained in this unsupervised manner from a large, varied protein sequence database, ProGen learns a universal, domain-independent representation of proteins that subsumes local and global structure motifs”
The paper relies on this without support.
HEWL is a suitable reference for native lysozyme catalytic activity.
“Hen egg white lysozyme (HEWL), a naturally-evolved exemplar protein, was measured as positive control”
The paper relies on this without support.
Model likelihoods for GANs are intractable.
“Since model likelihoods for GANs are intractable, we used discriminator scores corresponding to the probability at which the ProteinGAN discriminator predicted each sample was real as a ProteinGAN model score for each MDH sequence.”
The paper relies on this without support.
The average molecular weight of the fluorescein-labeled M. lysodeikticus cell wall substrate is unknown and the substrate is heterogeneous.
“(the average molecular weight of the fluorescein-labeled M. Lysodeikticus cell wall substrate was unknown and likely heterogeneous).”
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“synthetic proteins optimized for specific families of lysozymes using ProGen have exhibited catalytic efficiencies comparable to their naturally occurring counterparts with low sequence identity.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Madani et al. 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“AI models have already been used to design new CRISPR enzymes,53 functional proteins,128 and even entire prokaryotic genomes.51”
From How to build the virtual cell with artificial intelligence: Priorities and opportunities (2024) · cites this paper as [128]
The cited source says what the claim attributes to it.
In the citing paper
“The success of LLMs has revolutionized AI learning in natural language and image processing [75] and has been extended into the learning of biological sequence datasets [80].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [80]
The cited source says what the claim attributes to it.
In the citing paper
“the ProGen (Madani et al. 2023) language model employs deep learning techniques to produce protein sequences with predefined functionalities.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Madani et al. 2023
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Large language models generate functional protein sequences across diverse families > Abstract
“a state-of-the-art transformer-based conditional language model trained only with evolutionary sequence data”
§ Large language models generate functional protein sequences across diverse families > Methods > ProGen Training
“The model was initialized with pretrained weights of CTRL28, which was trained on an English language corpus.”
Citations
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Phaser crystallographic software
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Cited by
Sorted by citations
Items 201–300 of 1,012
Leveraging large language models for peptide antibiotic design
Guan, C., Fernandes, F. C., Franco, O. L., & de la Fuente‐Núñez, C. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102359 · Cited by 23
Tan, Y., Li, M., Zhou, Z., Tan, P., Yu, H., Fan, G., & Liang, H. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00884-3 · Cited by 22
“CPCProt [42] leverages a contrastive predictive coding loss, whereas ProGen [15, 16], UniRep [43], ProXLNet [18], ProtGPT2 [17], and Tranception [44] are pre-trained using next amino acid prediction tasks.”
BackgroundMethodKim, D. N., McNaughton, A. D., & Kumar, N. (2024) · Bioengineering · doi:10.3390/bioengineering11020185 · Cited by 35
“Language model-based sequence generation can involve the use of either an autoregressive model (e.g., ProtGPT [61], Pro-Gen [62]) or non-autoregressive language (e.g., AntiBERTy , ESM ) [63].”
MethodShen, J., Yu, Q., Chen, S., Tan, Q., Li, J., & Li, Y. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00576-2 · Cited by 32
“These models focused on analyzing these three functional regions (N-region, C-region, and H-region) in detail and were built by capturing the relationships between different regions of signal peptide [8, 9, 10, 11, 12].”
Siwek, J. C., Omelchenko, A. A., Chhibbar, P., Arshad, S., Rosengart, A., Nazarali, I., Patel, A., Nazarali, K., Rahimikollu, J., Tilstra, J. S., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02723-1 · Cited by 27
“Protein language models (pLMs) have been used for engineering synthetic protein sequences that function like natural proteins 4,5 and in niche contexts of predicting antibody evolution to better understand the immune repertoire 6 .”
Deep learning approaches for similarity computation: A survey
Yang, P., Wang, H., Yang, J., Qian, Z., Zhang, Y., & Lin, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3422484 · Cited by 28
Artificial intelligence in bioinformatics: a survey
Jiang, J., Li, Y., Cao, S., Shan, Y., Liu, Y., Fei, T., Yu, Y., Feng, Y., Li, Y., Li, Y., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf576 · Cited by 26
“ProtGPT2 [70] models the “language” of proteins to generate nov el sequences, while ProGen [121] and ProGen2 [71] train on vast genome and macro-genome datasets, producing diverse functional pr oteins without requiring additional fine-tuning.”
Gao, H., Guan, F.-F., Luo, B.-Y., Zhang, D., Liu, W., Shen, Y., Fan, L., Xu, G.-Q., Wang, Y., Tu, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64378-y · Cited by 26
Zhang, C., Wang, Q., Li, Y., Teng, A., Hu, G., Wuyun, Q., & Zheng, W. H. (2024) · Biomolecules · doi:10.3390/biom14121531 · Cited by 29
Adaptive immune receptor repertoire analysis
Mhanna, V., Bashour, H., Quý, K. L., Barennes, P., Rawat, P., Greiff, V., & Mariotti‐Ferrandiz, E. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-023-00284-1 · Cited by 66
DeepSS2GO: protein function prediction from secondary structure
Song, F. V., Su, J., Huang, S., Zhang, N., Li, K., Ni, M., & Liao, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae196 · Cited by 26
“…InterProScan [14], Multiple Sequence Alignment [15] and Position-Specific Scoring Matrix (PSSM) [16]) and those based on deep learning (e.g. Convolutional Neural Networks (CNNs) [17], Graph Neural Network (GNN) [18], Diffusion Network [19], Transformer [20, 21], Large Language Models [22], etc).”
Tan, Y., Wang, R.-L., Wu, B., Hong, L., & Zhou, B. (2025) · Bioinform. · doi:10.1093/bioinformatics/btaf189 · Cited by 25
“…protein language model (PLM) is to predict unknown AA types in a sequence from partial input, typically by autore-gressively generating AAs along the sequence index (Notin et al. 2022b, Madani et al. 2023) or by randomly masking a set of AAs and recovering them (Rives et al. 2021, Li et al. 2024).”
Heinzinger, M., & Rost, B. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102986 · Cited by 27
Drug Discovery in the Age of Artificial Intelligence: Transformative Target-Based Approaches
Patne, A. Y., Dhulipala, S. M., Lawless, W., Prakash, S., Mohapatra, S. S., & Mohapatra, S. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252212233 · Cited by 32
“Protein Language Models (PLMs) Some projects, like Omegafold, which applies PLMs based mostly on unaligned and unlabeled sequences [56], use residue pairs to create embeddings [57].”
Adapting protein language models for structure-conditioned designPreprint
Ruffolo, J. A., Bhatnagar, A., Beazer, J., Nayfach, S., Russ, J., Hill, E., Hussain, R., Gallagher, J. P., & Madani, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.03.606485 · Cited by 24
“Protein language models, which learn directly from sequences, have been shown to implicitly capture the structural and functional constraints of proteins (11, 13) and are capable of generating functional proteins (14–16).”
BackgroundSelf-citationESM All-Atom: Multi-scale Protein Language Model for Unified Molecular ModelingPreprint
Zheng, K., Long, S., Lu, T., Yang, J., Dai, X., Zhang, M., Nie, Z., Ma, W., & Zhou, H. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.04.583284 · Cited by 20
“Inspired by the large language model (LLM), ProtGPT2 (Ferruz et al., 2022), ProGen(Madani et al., 2023), and ProGen2 (Nijkamp et al., 2022) Unified Molecular Modeling Because of the huge scale difference between proteins and small molecules, it is challenging to model both of them in a unified…”
MethodHybridGCN for protein solubility prediction with adaptive weighting of multiple features
Chen, L., Wu, R., Zhou, F., Zhang, H., & Liu, J. K. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00788-8 · Cited by 25
Toward Automated Simulation Research Workflow through LLM Prompt Engineering Design
Liu, Z., Chai, Y., & Li, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01653 · Cited by 24
How artificial intelligence is reengineering protein engineering
Listgarten, J., & Jiang, H. (2026) · Science · doi:10.1126/science.aec8444 · Cited by 20
Kumar, A., Nazemi, F., Kodamana, H., Ramteke, M., & Bakshi, B. R. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c05955 · Cited by 24
Dhaarani, R., & Reddy, M. K. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100251 · Cited by 37
FusOn-pLM: a fusion oncoprotein-specific language model via adjusted rate masking
Vincoff, S., Goel, S., Kholina, K., Pulugurta, R., Vure, P., & Chatterjee, P. (2025) · Nature Communications · doi:10.1038/s41467-025-56745-6 · Cited by 20
Advances in artificial intelligence for artificial metamaterials
Si, L., Niu, R., Dang, C., Bao, X., Zhuang, Y., & Zhu, W. (2024) · APL Materials · doi:10.1063/5.0247369 · Cited by 37
Recent Advances in Peptide Drug Discovery: Novel Strategies and Targeted Protein Degradation
Vrbnjak, K., & Sewduth, R. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16111486 · Cited by 24
“Large language models, in particular, have been beneficial in this field, and protein language models have been developed to study the function and structure of proteins based only on their amino acid sequences [36,37].”
Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model
Tan, Y., Wang, R.-L., Wu, B., Hong, L., & Zhou, B. (2024) · arXiv.org · doi:10.48550/arxiv.2410.21127 · Cited by 24
“The objective of pre-training such a protein language model is to predict unknown AA types in a sequence from partial input, typically by autoregressively generating AAs along the sequence index [16,21] or by randomly masking a set of AAs and recovering them [13,27].”
BackgroundSurfPro: Functional Protein Design Based on Continuous Surface
Song, Z., Huang, T.-L., Li, L., & Jin, W.-G. (2024) · International Conference on Machine Learning · doi:10.48550/arxiv.2405.06693 · Cited by 24
“Due to the available massive sequence data, recent studies have successfully employed machine learning models pre-trained on such data, including ESM (Rives et al., 2021), ProtGPT2 (Ferruz et al., 2022) and ProGen (Madani et al., 2023).”
MethodUsing protein language models for protein interaction hot spot prediction with limited data
Sargsyan, K., & Lim, C. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05737-2 · Cited by 25
“For example, the protein language model, ProGen, can generate artificial functional proteins across protein families [19].”
BackgroundTang, S., Chen, X., Tong, X., & Zhu, L. (2025) · International Journal of Medical Sciences · doi:10.7150/ijms.112724 · Cited by 31
“By customizing tools to identify patient-specific infections or mutations, AI integration ensures more accurate and individualized diagnoses, ultimately improving treatment outcomes and therapeutic strategies [212, 213].”
Evaluating the representational power of pre-trained DNA language models for regulatory genomics
Tang, Z., Somia, N., Yu, Y., & Koo, P. K. (2025) · Genome biology · doi:10.1186/s13059-025-03674-8 · Cited by 71
“They may also enable more accurate predictions of the functional consequences of non-coding mutations, which can help to prioritize disease-associated variants.”
Large Language Models in HealthcarePreprint
Al-Garadi, M. A., Mungle, T., Ahmed, A., Sarker, A., Miao, Z., & Matheny, M. E. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2503.04748 · Cited by 24
Origins of life: The Protein Folding Problem all over again?
Kocher, C. D., & Dill, K. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315000121 · Cited by 26
Generative Artificial Intelligence: A Historical and Future Perspective
Kılınç, H. K., & Keçecioğlu, Ö. F. (2024) · Academic Platform Journal of Engineering and Smart Systems · doi:10.21541/apjess.1398155 · Cited by 32
“Analyzing high-dimensional genetic and biological data can contribute to disease analysis, identification of new drugs, drug design, and the development of personalized treatments [10], [11].”
ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods
Deotale, R., Srinivasan, A., Golestanian, M., Tian, Y., Zhang, T., Vlachos, P., & Gomez, H. (2026) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2026.118985 · Cited by 22
“This has been shown in studies such as BatteryBERT for battery research [54], BioBERT for biology [55], and ProGen for protein engineering [56].”
Phage therapy against Klebsiella pneumoniae: An evolving perspective
Xing, J., Zhang, H., Zheng, L., Zhao, J., Zhang, Y., Xu, Z., Zhai, Y., Hu, G., Li, J., & Wu, H. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108689 · Cited by 22
Jabalera, Y., Tascón, I., Samperio, S., López-Alonso, J. P., González-Lopez, M., Aransay, A. M., Abascal-Palacios, G., Beisel, C. L., Ubarretxena‐Belandia, I., & Pérez‐Jiménez, R. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02461-3 · Cited by 18
“…number of protein sequences in databases and the advent of methods for sequence alterations and design, including deep learning methods and language models 40–42 , have the potential to expand the universe of bio-molecules and revolutionize the fields of biotechnology and synthetic biology.”
Machine learning-aided design and screening of an emergent protein function in synthetic cells
Kohyama, S., Frohn, B. P., Babl, L., & Schwille, P. (2024) · Nature Communications · doi:10.1038/s41467-024-46203-0 · Cited by 23
Hutchinson, M., Ruffolo, J. A., Haskins, N., Iannotti, M., Vozza, G., Pham, T., Mehzabeen, N., Shandilya, H., Rickert, K., Croasdale-Wood, R., et al. (2024) · mAbs · doi:10.1080/19420862.2024.2362775 · Cited by 22
“23 Likewise, the thermostability predicting TemStaPro was trained using > 2 million protein sequences from organisms with known optimal growth temperatures from existing databases.”
MethodAtom-level generative foundation model for molecular interaction with pockets
Peng, X., Guo, F., Guo, R., Sun, J., Guan, J., Jia, Y., Xu, Y., Huang, Y., Zhang, M., Peng, J., et al. (2024) · doi:10.1101/2024.10.17.618827 · Cited by 22
“Although the text prompts can also be used to describe molecular tasks in the molecular models 29–31 , they do not capture the essence of the tasks, i.e., they cannot properly express the relationship among tasks.”
Illuminating the universe of enzyme catalysis in the era of artificial intelligence
Yang, J., Li, F.-Z., Long, Y., & Arnold, F. H. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101372 · Cited by 18
CPL‐Diff: A Diffusion Model for De Novo Design of Functional Peptide Sequences with Fixed Length
Luo, Z., Geng, A., Wei, L., Zou, Q., Cui, F., & Zhang, Z. (2025) · Advanced Science · doi:10.1002/advs.202412926 · Cited by 19
Forecasting Rare Language Model BehaviorsPreprint
Jones, E., Tong, M., Mu, J., Mahfoud, M., Leike, J., Grosse, R., Jared Kaplan, A., Fithian, W., Perez, E., & Sharma, M. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2502.16797 · Cited by 33
Artificial Intelligence Transforming Post-Translational Modification Research
Kim, D. N., Yin, T., Zhang, T., Im, A. K., Cort, J., Rozum, J. C., Pollock, D. D., Qian, W., & Feng, S. (2024) · Bioengineering · doi:10.3390/bioengineering12010026 · Cited by 19
Predicting adaptive immune receptor specificities by machine learning is a data generation problem
Mason, D. M., & Reddy, S. T. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.008 · Cited by 19
Application Evaluation and Performance-Directed Improvement of the Native and Engineered Biosensors
Li, M., Chen, Z., & Huo, Y. (2024) · ACS Sensors · doi:10.1021/acssensors.4c01072 · Cited by 24
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-TrainingPreprint
Chen, B., Bei, Z., Cheng, X., Li, P., Tang, J., & Song, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.10.598380 · Cited by 11
“As for protein design, PLMs including ProGen [10, 36] and ProtGPT2 [12], endowed by the autoregressive training regime, enable the generation of diverse and realistic protein sequences.”
Enzymatic synthesis of chiral alcohols using ketoreductases
Yuan, Q., Ma, L., Kong, W., Liu, J., Zhang, S., Yan, J., Bai, J., He, Y., Zhou, L., Liu, Y., et al. (2024) · Catalysis Reviews · doi:10.1080/01614940.2024.2313603 · Cited by 23
“…learning-based enzyme molecular modification, [183,194,195,226,227] high-throughput screening, and molecular biology technology, increasingly more chemo-enzymatic [226] and multi-enzyme cascade reactions will be integrated into the synthesis process of pharmaceutical and bioactive molecules.”
Semantical and geometrical protein encoding toward enhanced bioactivity and thermostability
Tan, Y., Zhou, B., Zheng, L., Fan, G., & Hong, L. (2025) · eLife · doi:10.7554/elife.98033.4 · Cited by 20
“However, the extensive, non-contiguous search space of AA combinations poses challenges for conventional methods, such as rational design ( Aprile et al., 2020 ) or directed evolution ( Wang et al., 2021 ), in efficiently and effectively identifying protein variants with desired properties.”
AI-Driven Chemical Design: Transforming the Sustainability of the Pharmaceutical Industry
Ruiz-Gonzalez, A. (2025) · Future Pharmacology · doi:10.3390/futurepharmacol5020024 · Cited by 23
“Moreover, Madani et al. [38] developed ProGen, that uses autoregressive LLMs to generate protein sequences with predictable functions.”
Conotoxins: Classification, Prediction, and Future Directions in Bioinformatics
Li, R., Yu, J., Ye, D.-X., Liu, S., Zhang, H., Lin, H., Feng, J., & Deng, K. (2025) · Toxins · doi:10.3390/toxins17020078 · Cited by 24
“Tools such as ProteinGAN [172], ProGen [173], ProtGPT [174], and ProteinMPNN [175] have achieved tremendous success in generating proteins and peptides.”
Computational Protein Science in the Era of Large Language Models (LLMs)Preprint
Fan, W., Zhou, Y., Wang, S., Yan, Y., Liu, H., Zhao, Q., Song, L., & Li, Q. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2501.10282 · Cited by 30
“Autoregressive pLMs (e.g., ProGen [35] and ProtGPT2 [26]) and diffusion frameworks (e.g., EvoDiff [78]) can generate protein sequences that exhibit similar physicochemical properties to natural proteins but have low homology to existing proteins.”
InfluentialZhang, H., Zhou, Y., Zhang, Z., Sun, H., Pan, Z., Mou, M., Zhang, W., Ye, Q., Hou, T., Li, H., et al. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c01793 · Cited by 15
Aspects of 6th generation sensing technology: from sensing to sense
Mazumdar, H., Khondakar, K. R., Das, S., & Kaushik, A. K. (2024) · Frontiers in Nanotechnology · doi:10.3389/fnano.2024.1434014 · Cited by 26
An integrative approach to protein sequence design through multiobjective optimizationPreprint
Hong, L., & Kortemme, T. (2024) · bioRxiv · doi:10.1101/2024.03.01.582670 · Cited by 20
Yan, Z., Ge, F., Liu, Y., Zhang, Y., Li, F., Song, J., & Yu, D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c02019 · Cited by 25
Mao, Q., Liu, Z., Liu, C., Li, Z., & Sun, J. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.05952 · Cited by 24
“These models represent a significant advancement over earlier, smaller Pre-trained Language Models (PLMs) [Gao et al. , 2021] in both size and capabilities, covering a wide range of tasks including understanding Zhu et al. [2023] and generating [Madani et al. , 2023] for natural language processing.”
MethodBioAgents: Bridging the gap in bioinformatics analysis with multi-agent systems
Mehandru, N., Hall, A. K., Melnichenko, O., Dubinina, Y., Tsirulnikov, D., Bamman, D., Alaa, A., Saponas, S., & Malladi, V. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-25919-z · Cited by 19
A suite of designed protein cages using machine learning and protein fragment-based protocols
Meador, K., Castells-Graells, R., Aguirre, R., Sawaya, M. R., Arbing, M. A., Sherman, T., Senarathne, C., & Yeates, T. O. (2024) · Structure · doi:10.1016/j.str.2024.02.017 · Cited by 19
Accelerating protein design by scaling experimental characterization
Qian, J., Milles, L. F., Wicky, B. I. M., Ragotte, R. J., Motmaen, A., Borst, A. J., Skotheim, R., Ols, S., Coventry, B., Li, X., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-76740-9 · Cited by 25
Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis
Vornholt, T., Stockinger, P., Mutný, M., Jeschek, M., Nestl, B. M., Oberdorfer, G., Osuna, S., Pleiss, J., Welner, D. H., Krause, A., et al. (2025) · ACS Central Science · doi:10.1021/acscentsci.5c00949 · Cited by 23
Wan, F.-P., Torres, M. D. T., Guan, C.-G., & de la Fuente-Núñez, C. (2025) · Nature Protocols · doi:10.1038/s41596-025-01144-w · Cited by 19
Tang, S., Zhang, Y., Tong, A., & Chatterjee, P. (2025) · arXiv.org · doi:10.48550/arxiv.2503.17361 · Cited by 19
“Generative modeling has transformed the design of biological sequences, enabling de novo protein design [1–3], DNA regulatory elements [4, 3], and peptides [5–7].”
Protein engineering in the deep learning era
Zhou, B., Tan, Y., Hu, Y., Zheng, L., Zhong, B., & Hong, L. (2024) · mLife · doi:10.1002/mlf2.12157 · Cited by 18
Yu, C., Fang, X., Tian, S., & Liu, H. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00973-w · Cited by 20
“The encoder is based on the self-attention mechanism [78], which has shown exceptional capability in extracting global dependency relationships from protein sequences [79–81].”
Extracellular vesicles for developing targeted hearing loss therapy
Pan, X., Li, Y., Huang, P., Staecker, H., & He, M. (2024) · Journal of Controlled Release · doi:10.1016/j.jconrel.2023.12.050 · Cited by 23
Likelihood-based Fine-tuning of Protein Language Models for Few-shot Fitness Prediction and DesignPreprint
Hawkins‐Hooker, A., Surana, S., Simons, J., Kmec, J., Bent, O., & Duckworth, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596156 · Cited by 10
“In this way, fitness prediction for a dataset of size N is converted from a regression problem with N labels into a binary classification problem with N × N labels.”
Grouped Speculative Decoding for Autoregressive Image Generation
So, J., Shin, J., 국현호, & Park, E. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01426 · Cited by 5
“This success has sparked widespread efforts to extend AR models to various domains [2, 13, 27].”
Generative AI: Current Status and Future Directions
Leong, L.-Y., Hew, T., Ooi, K., Tan, G., & Koohang, A. (2025) · Journal of Computational Information Systems · doi:10.1080/08874417.2025.2482571 · Cited by 18
BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent SystemsPreprint
Mehandru, N., Hall, A. K., Melnichenko, O., Dubinina, Y., Tsirulnikov, D., Bamman, D., Alaa, A., Saponas, S., & Malladi, V. S. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2501.06314 · Cited by 18
“Large language models (LLMs) have been applied to various domain-specific contexts, including scientific discovery in medicine [45, 49, 56], chemistry [6, 7], and biotechnology [31].”
Recent advances in deep learning and language models for studying the microbiome
Yan, B., Nam, Y., Li, L., Deek, R. A., Li, H., & Ma, S. (2025) · Frontiers in Genetics · doi:10.3389/fgene.2024.1494474 · Cited by 23
“This is more similar to GPT-style LLMs [Brown, 2020], whereby the task towards microbiome application often involves the generation of new, functional, and viable protein sequences [Ferruz et al., 2022, Madani et al., 2023, Jin et al., 2024].”
MethodDiverse Genomic Embedding Benchmark for functional evaluation across the tree of lifePreprint
West-Roberts, J. A., Kravitz, J., Jha, N., Cornman, A., & Hwang, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.10.602933 · Cited by 13
“…”biological foundation models” enabling functional interpretation of sequences has resulted in many modeling efforts in protein (Rives et al. (2021); Madani et al. (2023); Elnaggar et al. (2022)) and genomic (Dalla-Torre et al. (2023); Hwang et al. (2024); Nguyen et al. (2024)) sequence modalities.”
BackgroundZhai, J.-J., Gokaslan, A., Schiff, Y., Berthel, A., Liu, Z.-Y., Lai, W., Miller, Z. R., Scheben, A., Stitzer, M., Romay, M. C., et al. (2024) · bioRxiv · doi:10.1101/2024.06.04.596709 · Cited by 18
“For example, protein LMs, pre-trained on diverse protein sequences spanning the evolutionary tree, have shown successful applications in predicting atomic-level protein structure 7 and disease-causing variants 8 as well as in engineering protein design 9 .”
‘ChatGPT for CRISPR’ creates new gene-editing tools
Callaway, E. (2024) · Nature · doi:10.1038/d41586-024-01243-w · Cited by 22
Ngo, N. K., & Hy, T. S. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad3ee4 · Cited by 17
“Recent years have also seen the rise of pre-trained large language models for scientific discovery, which have achieved remarkable results in protein science [28, 29].”
BackgroundZhao, J., Zhang, C., & Luo, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.11.579859 · Cited by 7
“…fit a probability density model (e.g., hidden Markov models 8 , Potts models 9–19 , latent variable models 20–22 , and protein language models 23–29 ) on natural sequences – either homologous sequences of the target protein or massive natural protein sequences from databases such as UniProt 30…”
BackgroundMethodPraljak, N., Lian, X., Ranganathan, R., & Ferguson, A. L. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00261 · Cited by 18
Functional alignment of protein language models via reinforcement learning
Blalock, N., Seshadri, S., Nakamura, K., Babbar, A., Fahlberg, S. A., Kulkarni, A., & Romero, P. A. (2026) · Nature Communications · doi:10.1038/s41467-026-77557-2 · Cited by 19
Discrete Feynman-Kac CorrectorsPreprint
Hasan, M., Ohanesian, V., Gazizov, A., Bengio, Y., Aspuru‐Guzik, A., Bondesan, R., Skreta, M., & Neklyudov, K. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2601.10403 · Cited by 14
The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe
Zhang, G., Liu, C., Lu, J., Zhang, S., & Zhu, L. (2025) · Biology · doi:10.3390/biology14091268 · Cited by 22
“ProGen [109] is a 1.2 billion parameter neural network trained on 280 million sequences from over 19,000 annotated families that does not require structural data, while ProGen3 [27] scales this approach to 46 billion parameters pre-trained on 1.5 trillion amino-acid tokens drawn from 3.4 billion…”
InfluentialDeep learning in next-generation vaccine development for infectious diseases
Bhattacharya, M., Lo, Y.-H., Chatterjee, S., Das, A., Wen, Z., & Chakraborty, C. (2025) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2025.102586 · Cited by 19
“ProGen the conditional protein language model able to generates various artificial protein sequences across the protein families on the basis of input control tags Madani et al. 187 11.”
An astronomical question answering dataset for evaluating large language models
Li, J., Zhao, F., Chen, P., Xie, J., Zhang, X., Li, H., Chen, M., Wang, Y., & Zhu, M. (2025) · Scientific Data · doi:10.1038/s41597-025-04613-9 · Cited by 10
Toward deep learning sequence–structure co-generation for protein design
Wang, C., Alamdari, S., Domingo-Enrich, C., Amini, A. P., & Yang, K. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103018 · Cited by 18
Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection
Li, S., Tan, Y., Ke, S., Hong, L., & Zhou, B. (2024) · International Conference on Learning Representations · doi:10.48550/arxiv.2410.02647 · Cited by 17
“The two primary pre-training strategies are masked language model (Meier et al., 2021; Rao et al., 2021) and autoregressive generation (Ferruz et al., 2022; Madani et al., 2023).”
Kim, H., Yang, J. H., Chang, D.-G., Lenke, L. G., Pizones, J., Castelein, R., Watanabe, K., Trobisch, P., Mundis, G., Suh, S.-W., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/52001 · Cited by 17
ShennongMGS: An LLM-based Chinese Medication Guidance System
Dou, Y., Huang, Y., Zhao, X., Zou, H., Shang, J., Lu, Y., Yang, X., Xiao, J., & Peng, S. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3658451 · Cited by 13
“Additionally, LLMs are applied in medical research to aid researchers, such as the development of the protein-focused LLM ProGen by Ali Madani et al.[15], capable of generating protein sequences with predictable biological functions, analogous to producing grammatically and semantically correct…”
MethodBiocatalysis of CO2 and CH4: Key enzymes and challenges
Li, A., Cao, X., Fu, R., Guo, S., & Fei, Q. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108347 · Cited by 26
Generative AI for controllable protein sequence design: A survey
Zhu, Y., Kong, Z., Wu, J., Yin, M., Liu, W., Han, Y., Xu, H., Hsieh, C., Hou, T., & Wu, J. (2026) · npj Drug Discovery. · doi:10.1038/s44386-026-00054-5 · Cited by 14
“ProGen2 70 modelsarebasedonthe GPT-J architecture and have been scaled up to 6.4B parameters, exhibiting enhanced pro fi ciency in capturing the distribution of observed evolutionary sequences and making zero-shot fi tness predictions. xTrimoPGLM 71 adopts the backbone of the general language model and utilizes in-place…”
InfluentialVariational autoencoder for design of synthetic viral vector serotypes
Lyu, S., Sowlati‐Hashjin, S., & Garton, M. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00787-2 · Cited by 27
“More recently, deep learning models, including GANs 21,58 , VAEs 19,22,23,42 and large generative protein language models 20,38,59 , have been implemented to learn the complex constraints in biological sequence design.”
Leveraging ancestral sequence reconstruction for protein representation learning
Matthews, D. S., Spence, M. A., Mater, A. C., Nichols, J., Pulsford, S. B., Sandhu, M., Kaczmarski, J. A., Miton, C. M., Tokuriki, N., & Jackson, C. J. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00935-2 · Cited by 22
“Our analysis therefore sampled experimentally diverse scenarios (directed evolution, stochastic sequence sampling and combinatorically complete mutagenesis) over biologically distinct backgrounds (prokaryotic xenobiotic degrading enzymes and essential eukaryotic metabolic enzymes).”
InfluentialTen “Cheat Codes” for Measuring Oxidative Stress in Humans
Cobley, J. N., Margaritelis, N. V., Chatzinikolaou, P. N., Nikolaidis, M. G., & Davison, G. W. (2024) · Antioxidants · doi:10.3390/antiox13070877 · Cited by 17
BiRNA-BERT allows efficient RNA language modeling with adaptive tokenization
Tahmid, M. T., Shahgir, H. S., Mahbub, S., Dong, Y., & Bayzid, M. S. (2025) · Communications Biology · doi:10.1038/s42003-025-08982-0 · Cited by 16
“…residue-level downstream tasks, BiRNA-BERT introduces dual pretraining, supporting both sequence-level and residue-level tasks simultaneously. we discuss about the tokenization strategies for biological foundation models and applicability of using BPE tokenization for BiRNA-BERT (Section 2.2).”
InfluentialIntelligent enzyme design and hybrid catalytic systems: Driving innovation in biocatalysis
Qu, G., Zhong, F., Xu, J. F., Huang, X., & Sun, Z. (2025) · Green Synthesis and Catalysis · doi:10.1016/j.gresc.2025.12.002 · Cited by 14
Self-citationEBAMP: An efficient de novo broad-spectrum antimicrobial peptide discovery framework.
Zhao, Y.-X., Li, Q., Sun, M., Su, Y., Su, X., Jiang, L., Zhang, X.-R., Hu, Y.-Y., Wang, B., Yu, H.-R., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116215 · Cited by 16
Das, U. (2025) · ChemistrySelect · doi:10.1002/slct.202502448 · Cited by 17
Thornton, E. L., Boyle, J. T., Laohakunakorn, N., & Regan, L. (2025) · bioRxiv · doi:10.1101/2025.01.24.634768 · Cited by 16
“25,26 We show that the activity observed directly from CFPS is comparable to the activity observed with purified proteins.”
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges
Zhu, Y., Wu, J., Li, Q.-Y., Yan, J., Yin, M.-Z., Wu, W., Li, M., Ye, J.-P., Wang, Z., & Wu, J. (2024) · Neural Information Processing Systems · doi:10.48550/arxiv.2411.02120 · Cited by 16
“Motivated by the impressive advancements in PLMs for understanding and generating proteins [9, 34, 37, 60], we advocate for employing PLMs to approximate the Markov bridge process.”
MethodBenchmarking DNA Foundation Models for Genomic Sequence Classification
Feng, H.-N., Wu, L., Zhao, B., Huff, C., Zhang, J.-J., Wu, J., Lin, L.-F., Wei, P., & Wu, C. (2024) · bioRxiv · doi:10.1101/2024.08.16.608288 · Cited by 16
Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates
Song, Z., Zhao, Y.-L., Shi, W., Jin, W.-G., Yang, Y., & Li, L. (2024) · International Conference on Machine Learning · doi:10.48550/arxiv.2405.08205 · Cited by 16
“Owing to the availability of large-scale data, some models are trained on extensive protein sequences from diverse families (Ferruz et al., 2022) or incorporate additional control tags for enhanced modeling (Madani et al., 2023).”
Integration of cognitive tasks into artificial general intelligence test for large models
Qu, Y., Wei, C., Du, P., Che, W., Zhang, C., Ouyang, W., Bian, Y., Xu, F., Hu, B., Du, K., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109550 · Cited by 20
“Large language models (LLMs) have made impressiveprogress in a short time, reaching a high level of proficiency in human language, 1 mathematics, 2,3 physics, 4 biology, 5,6 and clinic, 7–9 which illuminates the path toward artificial general intelligence (AGI).”
BackgroundUsing big sequencing data to identify chronic SARS-Coronavirus-2 infections
Harari, S., Miller, D., Fleishon, S., Burstein, D., & Stern, A. (2024) · Nature Communications · doi:10.1038/s41467-024-44803-4 · Cited by 37
“As the volume of biological sequence data continues to increase (for example, over 4 million SARS-CoV-2 sequences were deposited during the last year), language models are emerging as valuable tools 30,57,58 .”
Marchal, D. G., Schulz, L., Schuster, I., Ivanovska, J., Paczia, N., Prinz, S., Zarzycki, J., & Erb, T. (2023) · ACS Synthetic Biology · doi:10.1021/acssynbio.3c00403 · Cited by 16
Metadata
- Authors
- Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., Fraser, J. S., & Naik, N.
- Affiliations
- Salesforce (United States) (US)
- University of California, San Francisco (US)
- Howard Hughes Medical Institute (US)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Madani, Ali
- Naik, Nikhil
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2023-01-26
- Volume
- 41
- Issue
- 8
- Pages
- 1099-1106
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC10400306
- OpenAlex
- W4318071656
- Cited by
- 1,078
- References
- 80
- Citations by year
- 2026: 314 · 2025: 320 · 2024: 302 · 2023: 141
- FWCI
- 95.97
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (GM123159)
- 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 › Molecular Biology › Enzyme Catalysis and Immobilization
- Keywords
- large language models, protein sequence generation, protein design, protein families, lysozyme, chorismate mutase, malate dehydrogenase, catalytic efficiency, controllable generation, protein engineering
- MeSH terms
- Amino Acid Sequence, Chorismate Mutase, Estrogens, Conjugated (USP), Language, Proteins
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 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (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.
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