ArticleFrontiers in bioinformatics2023
Prediction of polyspecificity from antibody sequence data by machine learning.
Article in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Predicting non-specific binding of VHHs using machine learning models with cluster-aware validation.mAbs · 2026Article
- The evolution of display technologies for antibody drug discovery.Trends in biotechnology · 2026Review
- Medical software for precision diagnostics of infection with immunoprofiling and artificial intelligence.Journal of translational medicine · 2026Review
- A robust and scalable immunoadsorbent platform based on the high-affinity M03-IgG antibody for targeted IL-6 Removal in cytokine storm.Frontiers in immunology · 2026Article
- Machine learning in AIRR diagnostics: Advances and applications.Immunoinformatics (Amsterdam, Netherlands) · 2025Article
- Enhancing polyreactivity prediction of preclinical antibodies through fine-tuned protein language models.Journal of pharmaceutical analysis · 2025Article
- Human antibody polyreactivity is governed primarily by the heavy-chain complementarity-determining regions.Cell reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Antibodies are generated with great diversity in nature resulting in a set of molecules, each optimized to bind a specific target. Taking advantage of their diversity and specificity, antibodies make up for a large part of recently developed biologic drugs. For therapeutic use antibodies need to fulfill several criteria to be safe and efficient. Polyspecific antibodies can bind structurally unrelated molecules in addition to their main target, which can lead to side effects and decreased efficacy in a therapeutic setting, for example via reduction of effective drug levels. Therefore, we created a neural-network-based model to predict polyspecificity of antibodies using the heavy chain variable region sequence as input. We devised a strategy for enriching antibodies from an immunization campaign either for antigen-specific or polyspecific binding properties, followed by generation of a large sequencing data set for training and cross-validation of the model. We identified important physico-chemical features influencing polyspecificity by investigating the behaviour of this model. This work is a machine-learning-based approach to polyspecificity prediction and, besides increasing our understanding of polyspecificity, it might contribute to therapeutic antibody development.
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Registered trials
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