ArticleScientific reports2021
Antibody design using LSTM based deep generative model from phage display library for affinity maturation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed, 142 citations in OpenAlex.
- Multidimensional maturation of antibody variable domains with machine-learning assistance.mAbs · 2026Article
- The Evolution of Artificial Intelligence in Antibody Design: From Structure-Based Engineering to Generative Models.Antibodies (Basel, Switzerland) · 2026Review
- The evolution of display technologies for antibody drug discovery.Trends in biotechnology · 2026Review
- MAMMAL - Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery.npj drug discovery · 2026Article
- AI-Driven BCR Modeling for Precision Immunology.International journal of molecular sciences · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- GeoGAD: geometry-aware antibody design framework for complementarity-determining region precision engineering.Bioinformatics (Oxford, England) · 2026Article
- Advances in Therapeutic Antibody Discovery and Development Targeting G Protein-Coupled Receptors.Pharmacology research & perspectives · 2026Review
- Machine learning enables efficient and effective affinity maturation of nanobodies.bioRxiv : the preprint server for biology · 2026Article
- Antibody Affinity Maturation by Computational Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Detecting statistical interactions in immune receptor data: a comparative study.Journal of applied statistics · 2026Article
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Antibody-Drug Conjugates in Breast Cancer: Navigating Innovations, Overcoming Resistance, and Shaping Future Therapies.Biomedicines · 2025Review
- Deep learning in next-generation vaccine development for infectious diseases.Molecular therapy. Nucleic acids · 2025Review
- Profiling antigen-binding affinity of B cell repertoires in tumors by deep learning predicts immune-checkpoint inhibitor treatment outcomes.Nature cancer · 2025Article
- Using extension-based mRNA display to design antibody-like proteinogenic peptides for human PD-L1.Protein science : a publication of the Protein Society · 2025Article
- Significantly enhancing human antibody affinity via deep learning and computational biology-guided single-point mutations.Briefings in bioinformatics · 2025Article
- Nanodesigner: resolving the complex-CDR interdependency with iterative refinement.Journal of cheminformatics · 2025Article
- Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular evolution is an important step in the development of therapeutic antibodies. However, the current method of affinity maturation is overly costly and labor-intensive because of the repetitive mutation experiments needed to adequately explore sequence space. Here, we employed a long short term memory network (LSTM)-a widely used deep generative model-based sequence generation and prioritization procedure to efficiently discover antibody sequences with higher affinity. We applied our method to the affinity maturation of antibodies against kynurenine, which is a metabolite related to the niacin synthesis pathway. Kynurenine binding sequences were enriched through phage display panning using a kynurenine-binding oriented human synthetic Fab library. We defined binding antibodies using a sequence repertoire from the NGS data to train the LSTM model. We confirmed that likelihood of generated sequences from a trained LSTM correlated well with binding affinity. The affinity of generated sequences are over 1800-fold higher than that of the parental clone. Moreover, compared to frequency based screening using the same dataset, our machine learning approach generated sequences with greater affinity.
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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 Socratic graph.