ArticleFrontiers in genetics2022
SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- Germline-aware deep learning models and benchmarks for predicting antibody VH-VL pairing.mAbs · 2025Article
- Article
- Enhancing polyreactivity prediction of preclinical antibodies through fine-tuned protein language models.Journal of pharmaceutical analysis · 2025Article
- Can artificial intelligence uncover the bioactive peptides' benefits for human health and knowledge? A narrative review.Frontiers in nutrition · 2025Review
- DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024Article
- A mini review on recent progress of microfluidic systems for antibody development.Journal of diabetes and metabolic disorders · 2024Review
- ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.Frontiers in genetics · 2024Article
- Review
- Decreasing hydrophobicity or shielding hydrophobic areas of CH2 attenuates low pH-induced IgG4 aggregation.Frontiers in bioengineering and biotechnology · 2023Article
- A Review of Transition Metal Dichalcogenides-Based Biosensors.Frontiers in bioengineering and biotechnology · 2022Review
- Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Therapeutic antibodies play a crucial role in the treatment of various diseases. However, the success rate of antibody drug development is low partially because of unfavourable biophysical properties of antibody drug candidates such as the high aggregation tendency, which is mainly driven by hydrophobic interactions of antibody molecules. Therefore, early screening of the risk of hydrophobic interaction of antibody drug candidates is crucial. Experimental screening is laborious, time-consuming, and costly, warranting the development of efficient and high-throughput computational tools for prediction of hydrophobic interactions of therapeutic antibodies. In the present study, 131 antibodies with hydrophobic interaction experiment data were used to train a new support vector machine-based ensemble model, termed SSH2.0, to predict the hydrophobic interactions of antibodies. Feature selection was performed against CKSAAGP by using the graph-based algorithm MRMD2.0. Based on the antibody sequence, SSH2.0 achieved the sensitivity and accuracy of 100.00 and 83.97%, respectively. This approach eliminates the need of three-dimensional structure of antibodies and enables rapid screening of therapeutic antibody candidates in the early developmental stage, thereby saving time and cost. In addition, a web server was constructed that is freely available at http://i.uestc.edu.cn/SSH2/.
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Registered trials
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