ArticleScientific reports2023
Low-data interpretable deep learning prediction of antibody viscosity using a biophysically meaningful representation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- 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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- Artificial Intelligence and Machine Learning Approaches in Designing Immunotherapy in Cancer.Cancer treatment and research · 2025Review
- DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability.Computational and structural biotechnology journal · 2024Article
- Sequence-Based Viscosity Prediction for Rapid Antibody Engineering.Biomolecules · 2024Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
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Authors and funding
3 authors.
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Abstract
Deep learning, aided by the availability of big data sets, has led to substantial advances across many disciplines. However, many scientific problems of practical interest lack sufficiently large datasets amenable to deep learning. Prediction of antibody viscosity is one such problem where deep learning methods have not yet been explored due to the relative scarcity of relevant training data. In this work, we overcome this limitation using a biophysically meaningful representation that enables us to develop generalizable models even under limited training data. We present, PfAbNet-viscosity, a 3D convolutional neural network architecture, to predict high-concentration viscosity of therapeutic antibodies. We show that with the electrostatic potential surface of the antibody variable region as the only input to the network, the models trained on as few as couple dozen datapoints can generalize with high accuracy. Our feature attribution analysis shows that PfAbNet-viscosity has learned key biophysical drivers of viscosity. The applicability of our approach to other biological systems is discussed.
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