ArticlemAbs
Reduction of monoclonal antibody viscosity using interpretable machine learning.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
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- A Review on Applications of Artificial Intelligence (AI) in Monoclonal Antibody (mAb) Manufacturing.Molecules (Basel, Switzerland) · 2026Review
- The evolution of display technologies for antibody drug discovery.Trends in biotechnology · 2026Review
- Fab-Fc and Fab-Fab interactions of variable strength and valency contribute to the high concentration viscosity of IgGProceedings of the National Academy of Sciences of the United States of America · 2026Article
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- PROPERMAB: an integrative framework formAbs · 2025Article
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- Viscosity of concentrated antibodies from a dynamic model of electrostatics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Opportunities in formulation development of antibody-based therapeutics.Antibody therapeutics · 2025Article
- Explainable Artificial Intelligence in the Field of Drug Research.Drug design, development and therapy · 2025Review
- Hybrid Mass Spectrometry Applied across the Production of Antibody Biotherapeutics.Journal of the American Society for Mass Spectrometry · 2025Article
- A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles.Computational and structural biotechnology journal · 2024Article
- Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability.Communications biology · 2024Article
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Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Early identification of antibody candidates with drug-like properties is essential for simplifying the development of safe and effective antibody therapeutics. For subcutaneous administration, it is important to identify candidates with low self-association to enable their formulation at high concentration while maintaining low viscosity, opalescence, and aggregation. Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. Our model was trained on antibody viscosity data (>100 mg/mL mAb concentration) obtained at a common formulation pH (pH 5.2), and it identifies three key Fv features of antibodies linked to viscosity, namely their isoelectric points, hydrophobic patch sizes, and numbers of negatively charged patches. Of the three features, most predicted antibodies at risk for high viscosity, including antibodies with diverse antibody germlines in our study (79 mAbs) as well as clinical-stage IgG1s (94 mAbs), are those with low Fv isoelectric points (Fv pIs < 6.3). Our model identifies viscous antibodies with relatively high accuracy not only in our training and test sets, but also for previously reported data. Importantly, we show that the interpretable nature of the model enables the design of mutations that significantly reduce antibody viscosity, which we confirmed experimentally. We expect that this approach can be readily integrated into the drug development process to reduce the need for experimental viscosity screening and improve the identification of antibody candidates with drug-like properties.
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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.