ArticlemAbs
BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.
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Who cites it
116 citing papers in PubMed, 1 synthesis or guideline pooled it, 183 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026Review
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Antibody format matters: A comparative analysis of VHH and scFv domains reveals superior in vivo CAR T cell function with VHH domains.Molecular therapy : the journal of the American Society of Gene Therapy · 2026Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- Targeting CD24 Activates Macrophages to Reduce Tumor Burden in Preclinical Models of Solid Tumors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- An experimentally validated structure-based computational framework for humanisation of anti-orthopoxvirus antibodies.EBioMedicine · 2026Article
- Multi-objective antibody design and optimization using machine learning.Nature reviews bioengineering · 2026Article
- CLDN18.2 antibody design with protein language models: A deep learning optimization framework.PLoS computational biology · 2026Article
- Evidence-aware comparison of sequence-centric machine learning for antibody discovery and optimization.Briefings in bioinformatics · 2026Article
- Efficient generation of epitope-targeted antibodies with Germinal.Nature biotechnology · 2026Article
- Article
- 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
- Development of a machine-learning based diagnosis procedure to distinguish aortic dissection from non-ST-elevation myocardial infarction.BMC medical informatics and decision making · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- BCRInsight: an antibody language model to decode biological signals from BCR sequences.Briefings in bioinformatics · 2026Article
- A Unified Dataset for Antibody and Nanobody Design Including Sequence, Structure, and Binding Affinity Data.Scientific data · 2026Article
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- Advances in Therapeutic Antibody Discovery and Development Targeting G Protein-Coupled Receptors.Pharmacology research & perspectives · 2026Review
56 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
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary. To predict the immunogenicity risk, the human-likeness of sequences can be evaluated using existing humanness scores, but these lack diversity, granularity or interpretability. Meanwhile, immune repertoire sequencing has generated rich antibody libraries such as the Observed Antibody Space (OAS) that offer augmented diversity not yet exploited for antibody engineering. Here we present BioPhi, an open-source platform featuring novel methods for humanization (Sapiens) and humanness evaluation (OASis). Sapiens is a deep learning humanization method trained on the OAS using language modeling. Based on an
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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.