ArticleBioinformatics (Oxford, England)2024
p-IgGen: a paired antibody generative language model.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- Evidence-aware comparison of sequence-centric machine learning for antibody discovery and optimization.Briefings in bioinformatics · 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
- Article
- LICHEN enables light-chain immunoglobulin sequence generation conditioned on the heavy chain and experimental needs.Communications biology · 2026Article
- ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains.Nature methods · 2026Article
- Germline-aware deep learning models and benchmarks for predicting antibody VH-VL pairing.mAbs · 2025Article
- Nucleotide context models outperform protein language models for predicting antibody affinity maturation.bioRxiv : the preprint server for biology · 2025Article
- A curriculum learning approach to training antibody language models.PLoS computational biology · 2025Article
- A curriculum learning approach to training antibody language models.PLoS computational biology · 2025Article
- A Sitewise Model of Natural Selection on Individual Antibodies via a Transformer-Encoder.Molecular biology and evolution · 2025Article
- Focused learning by antibody language models using preferential masking of non-templated regions.Patterns (New York, N.Y.) · 2025Article
- An expandable synthetic library of human paired antibody sequences.PLoS computational biology · 2025Article
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- A comprehensive overview of recent advances in generative models for antibodies.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
summaryA key challenge in antibody drug discovery is designing novel sequences that are free from developability issues-such as aggregation, polyspecificity, poor expression, or low solubility. Here, we present p-IgGen, a protein language model for paired heavy-light chain antibody generation. The model generates diverse, antibody-like sequences with pairing properties found in natural antibodies. We also create a finetuned version of p-IgGen that biases the model to generate antibodies with 3D biophysical properties that fall within distributions seen in clinical-stage therapeutic antibodies. AVAILABILITY AND IMPLEMENTATION: The model and inference code are freely available at www.github.com/oxpig/p-IgGen. Cleaned training data are deposited at doi.org/10.5281/zenodo.13880874.
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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.