ArticleProtein engineering, design & selection : PEDS2019
Quantifying the nativeness of antibody sequences using long short-term memory networks.
Article in Protein engineering, design & selection : PEDS, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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.
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Who cites it
24 citing papers in PubMed.
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025Article
- T-SCAPE: T cell immunogenicity scoring via cross-domain aided predictive engine.Science advances · 2025Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- SITA: Predicting site-specific immunogenicity for therapeutic antibodies.Journal of pharmaceutical analysis · 2025Article
- Nanobodies: From Discovery to AI-Driven Design.Biology · 2025Review
- Article
- Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors.International journal of molecular sciences · 2024Review
- DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024Article
- For antibody sequence generative modeling, mixture models may be all you need.Bioinformatics (Oxford, England) · 2024Article
- The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design.Journal of biomedical science · 2024Review
- Building Representation Learning Models for Antibody Comprehension.Cold Spring Harbor perspectives in biology · 2024Review
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- Prospects for the computational humanization of antibodies and nanobodies.Frontiers in immunology · 2024Review
- nanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space.Bioinformatics advances · 2024Article
- The RESP AI model accelerates the identification of tight-binding antibodies.Nature communications · 2023Article
- Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.Briefings in bioinformatics · 2022Review
- Detection of dementia on voice recordings using deep learning: a Framingham Heart Study.Alzheimer's research & therapy · 2021Observational
- Toward Drug-Like Multispecific Antibodies by Design.International journal of molecular sciences · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Antibodies often undergo substantial engineering en route to the generation of a therapeutic candidate with good developability properties. Characterization of antibody libraries has shown that retaining native-like sequence improves the overall quality of the library. Motivated by recent advances in deep learning, we developed a bi-directional long short-term memory (LSTM) network model to make use of the large amount of available antibody sequence information, and use this model to quantify the nativeness of antibody sequences. The model scores sequences for their similarity to naturally occurring antibodies, which can be used as a consideration during design and engineering of libraries. We demonstrate the performance of this approach by training a model on human antibody sequences and show that our method outperforms other approaches at distinguishing human antibodies from those of other species. We show the applicability of this method for the evaluation of synthesized antibody libraries and humanization of mouse antibodies.
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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.