ArticleNature communications2021
Protein design and variant prediction using autoregressive generative models.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
167 citing papers in PubMed, 341 citations in OpenAlex.
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- A Synthetic Platform for Antibody Junctional Diversification Beyond Natural Constraints.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Discovery and design of potent cell surface display elements.Nature biotechnology · 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
- Scaling SMILES-based chemical language models for therapeutic peptide engineering.bioRxiv : the preprint server for biology · 2026Article
- Deep Learning-Guided Reverse Translation Enhances Soluble Expression of Recombinant Proteins inInternational journal of molecular sciences · 2026Article
- Manufacturing-aware generative models enable petascale synthesis of designed DNA.Nature biotechnology · 2026Article
- Overcoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modeling.PLoS computational biology · 2026Article
- Computational evolution of poly(U) polymerase for efficient and controlled RNA oligonucleotide synthesis.Nucleic acids research · 2026Article
- Antibody Affinity Maturation by Computational Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Artificial Intelligence in Selected Domains of Drug Discovery: A Critical Narrative Review.Drug design, development and therapy · 2026Review
- Proteome-wide model for human disease genetics.Nature genetics · 2025Article
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- Multimodal diffusion for joint design of protein sequence and structure.Protein science : a publication of the Protein Society · 2025Article
- Evaluating the significance of embedding-based protein sequence alignment with clustering and double dynamic programming for remote homology.Scientific reports · 2025Article
- Overcoming Extrapolation Challenges of Deep Learning by Incorporating Physics in Protein Sequence-Function Modeling.bioRxiv : the preprint server for biology · 2025Article
107 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 10
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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.