ArticleBioinformatics (Oxford, England)2024
For antibody sequence generative modeling, mixture models may be all you need.
Article in Bioinformatics (Oxford, England), 2024. 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 7 papers.
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Who cites it
7 citing papers in PubMed.
- A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.Nature biotechnology · 2026Article
- Mixture diffusion model for multimodal antibody design.Briefings in bioinformatics · 2026Article
- ANARCII enables alignment-free antigen receptor numbering using a generalised language model.Communications biology · 2026Article
- Pioneer: a synthetic human antibody phage display library for rapid therapeutic lead generation.mAbs · 2025Article
- RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery.bioRxiv : the preprint server for biology · 2025Article
- RIOT-Rapid Immunoglobulin Overview Tool-annotation of nucleotide and amino acid immunoglobulin sequences using an open germline database.Briefings in bioinformatics · 2024Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
motivationAntibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity.
resultsIn this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature. AVAILABILITY AND IMPLEMENTATION: All tools developed in this study are available at https://github.com/Wang-lab-UCSD/AntPack.
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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.