ArticleCell systems2023
ProGen2: Exploring the boundaries of protein language models.
Article in Cell systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 223 papers, 1 of them a synthesis that pooled it.
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Who cites it
223 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Machine-learning in optimization of CRISPR technology.Machine learning. Health · 2026Review
- Engineering peroxygenases for pharmaceutical applications: recent advances and future perspectives.Chemical science · 2026Review
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Epigenetic insights into extreme longevity in the world's oldest terrestrial animal, Jonathan.Science advances · 2026Article
- Democratizing protein language model training, sharing and collaboration.Nature biotechnology · 2026Article
- The global patent landscape of CRISPR-Cas applications in medicine.Nature biotechnology · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- AI-designed OpenCRISPR-1 performs robust knockout, base editing, and prime editing in rice.The New phytologist · 2026Article
- Aligning protein-generative models to experimental fitness with ProteinDPO.Nature methods · 2026Article
- How to build an AI-driven digital organism.Nature medicine · 2026Review
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- SynBioGPT2: A dynamic reasoning framework enables high-fidelity design of microbial cell factories.Biodesign research · 2026Article
- MBPBERT: A Large Language Model for Metal-Binding Peptide Discovery.Interdisciplinary sciences, computational life sciences · 2026Article
- Accelerating inference in genomic and proteomic foundation models via speculative decoding.Bioinformatics (Oxford, England) · 2026Article
- Evolutionary profiles for protein fitness prediction.Bioinformatics (Oxford, England) · 2026Article
- Discovery and protein language model-guided design of hyperactive transposases.Nature biotechnology · 2026Article
- Property guidance for protein sequence generative models with ProteinGuide.Nature biotechnology · 2026Article
163 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial-intelligence-driven protein design. However, we lack a sufficient understanding of how very large-scale models and data play a role in effective protein model development. We introduce a suite of protein language models, named ProGen2, that are scaled up to 6.4B parameters and trained on different sequence datasets drawn from over a billion proteins from genomic, metagenomic, and immune repertoire databases. ProGen2 models show state-of-the-art performance in capturing the distribution of observed evolutionary sequences, generating novel viable sequences, and predicting protein fitness without additional fine-tuning. As large model sizes and raw numbers of protein sequences continue to become more widely accessible, our results suggest that a growing emphasis needs to be placed on the data distribution provided to a protein sequence model. Our models and code are open sourced for widespread adoption in protein engineering. A record of this paper's Transparent Peer Review process is included in the supplemental information.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.