ArticleNature communications2021
Efficient generative modeling of protein sequences using simple autoregressive models.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- A structure-aware generative AI framework for revealing functional relationships in protein families.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Towards Safer Antimicrobial Peptide Therapeutics: A Predictive-Generative Framework Targeting ESKAPE Pathogens.Probiotics and antimicrobial proteins · 2026Article
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Targeting the Undruggable: Deep Learning-Driven Design of Peptide Therapeutics in Cancer.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Dual role of glycosylation in resistance to CD4-binding site broadly neutralizing antibodies.Journal of virology · 2026Article
- Deep learning-driven integrated pipeline for de novo design and synthesis of antimicrobial peptides.npj drug discovery · 2026Article
- Descent from a common ancestor restricts exploration of protein sequence space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Functional bottlenecks can emerge from non-epistatic underlying traits.PLoS computational biology · 2026Article
- Deep models of protein evolution in time generate realistic evolutionary trajectories and functional proteins.bioRxiv : the preprint server for biology · 2026Article
- Controllable protein design via autoregressive direct coupling analysis conditioned on principal components.PLoS computational biology · 2026Article
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models.Physical review research · 2025Article
- Article
- Harnessing advanced computational approaches to design novel antimicrobial peptides against intracellular bacterial infections.Bioactive materials · 2025Article
- Explainability of Protein Deep Learning Models.International journal of molecular sciences · 2025Article
- PRESCOTT: a population aware, epistatic, and structural model accurately predicts missense effects.Genome biology · 2025Article
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Computational scoring and experimental evaluation of enzymes generated by neural networks.Nature biotechnology · 2025Article
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between 10
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