ArticleiScience2022
Antibody apparent solubility prediction from sequence by transfer learning.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 19 citations in OpenAlex.
- Review
- Strategies for enhancing protein solubility: methods, applications, and prospects.NPJ science of food · 2026Review
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- PROPERMAB: an integrative framework formAbs · 2025Article
- Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- DeepSP: Deep learning-based spatial properties to predict monoclonal antibody stability.Computational and structural biotechnology journal · 2024Article
- DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024Article
- Fine-tuning protein language models boosts predictions across diverse tasks.Nature communications · 2024Article
- Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability.Communications biology · 2024Article
- Building Representation Learning Models for Antibody Comprehension.Cold Spring Harbor perspectives in biology · 2024Review
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity.Computational and structural biotechnology journal · 2022Article
- Article
- Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches.Biotechnology advancesReview
- Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing therapeutic monoclonal antibodies (mAbs) for the subcutaneous administration requires identifying mAbs with superior solubility that are amenable for high-concentration formulation. However, experimental screening is often material and labor intensive. Here, we present a strategy (named solPredict) that employs the embeddings from pretrained protein language modeling to predict the apparent solubility of mAbs in histidine (pH 6.0) buffer. A dataset of 220 diverse, in-house mAbs were used for model training and hyperparameter tuning through 5-fold cross validation. solPredict achieves high correlation with experimental solubility on an independent test set of 40 mAbs. Importantly, solPredict performs well for both IgG1 and IgG4 subclasses despite the distinct solubility behaviors. This approach eliminates the need of 3D structure modeling of mAbs, descriptor computation, and expert-crafted input features. The minimal computational expense of solPredict enables rapid, large-scale, and high-throughput screening of mAbs using sequence information alone during early antibody discovery.
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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.