ArticleCell systems2023
IgLM: Infilling language modeling for antibody sequence design.
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 72 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
72 citing papers in PubMed.
- Aligning protein-generative models to experimental fitness with ProteinDPO.Nature methods · 2026Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- Article
- What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study.bioRxiv : the preprint server for biology · 2026Article
- H3BERTa: A CDR-H3-specific language model for antibody repertoire analysis.Patterns (New York, N.Y.) · 2026Article
- Evidence-aware comparison of sequence-centric machine learning for antibody discovery and optimization.Briefings in bioinformatics · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- AbTune: layer-wise selective fine-tuning of protein language models for antibodies.Briefings in bioinformatics · 2026Article
- OpenGerminal: an open-source implementation of the Germinal antibody design pipeline.bioRxiv : the preprint server for biology · 2026Article
- Efficient generation of epitope-targeted antibodies with Germinal.Nature biotechnology · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Scaling antibody language models improves structure aware representation for antibody engineering.Communications biology · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Context-aware multi-property antibody predictor: a novel framework integrating text and protein language models.NPJ systems biology and applications · 2026Article
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- Manufacturing-aware generative models enable petascale synthesis of designed DNA.Nature biotechnology · 2026Article
- LICHEN enables light-chain immunoglobulin sequence generation conditioned on the heavy chain and experimental needs.Communications biology · 2026Article
- DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions.bioRxiv : the preprint server for biology · 2026Article
- GeoGAD: geometry-aware antibody design framework for complementarity-determining region precision engineering.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
12 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
3 authors.
Funding
Abstract
Discovery and optimization of monoclonal antibodies for therapeutic applications relies on large sequence libraries but is hindered by developability issues such as low solubility, high aggregation, and high immunogenicity. Generative language models, trained on millions of protein sequences, are a powerful tool for the on-demand generation of realistic, diverse sequences. We present the Immunoglobulin Language Model (IgLM), a deep generative language model for creating synthetic antibody libraries. Compared with prior methods that leverage unidirectional context for sequence generation, IgLM formulates antibody design based on text-infilling in natural language, allowing it to re-design variable-length spans within antibody sequences using bidirectional context. We trained IgLM on 558 million (M) antibody heavy- and light-chain variable sequences, conditioning on each sequence's chain type and species of origin. We demonstrate that IgLM can generate full-length antibody sequences from a variety of species and its infilling formulation allows it to generate infilled complementarity-determining region (CDR) loop libraries with improved in silico developability profiles. A record of this paper's transparent peer review process is included in the supplemental information.
Indexed as
Identifiers
What OpenQuestion holds
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.