Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength
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Abstract
The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.
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Qian, J., Yang, L., Duan, Z., Wang, R., & Qi, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c01390 · Cited by 4 · see citing works
Jiang, Y., Zhou, K., He, H., Zhou, Y., Tang, J., Guan, T., Chen, S., Zhou, T., Tang, Y. Z., Wang, A., et al. (2024) · AAPS PharmSciTech · doi:10.1208/s12249-024-02973-w · Cited by 4 · see citing works
An interpretable molecular framework for predicting cancer driver missense mutations
Yang, Y., Sun, W., Liu, Y., Zhang, J., & Li, M. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2524289123 · Cited by 3 · see citing works
Self-citationJawarkar, R. D., Deshmukh, P. K., Mandwale, B., & Long Chaiu, M. I. N. G. (2026) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2026.100108 · Cited by 2 · see citing works
Screening clusters of charged residues in plants’ mitochondrial proteins and biological significance
Nebli, S., Rebaï, A., & Ayadi, I. (2024) · Mitochondrion · doi:10.1016/j.mito.2024.101938 · Cited by 1 · see citing works
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CNN-Based Image Processing for Recognizing Balinese Characters on Palm Leaves
Nugraha, I., Ginantra, N. L. W. S. R., Wiguna, I. K. A. G., & Indrawan, I. G. A. (2024) · International Conference on Informatics and Computing (ICIC) · doi:10.1109/icic64337.2024.10957575
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D81 Mutants Reveal Hidden EF-Tu Diversity while Natural Sequences Preserve Aspartate
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Self-citation
- Authors
- Zheng, F., Jiang, X., Wen, Y., Yang, Y., & Li, M.
- Affiliations
- Soochow University (CN)
- Corresponding author
- Li, Minghui
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2023-12-30
- Volume
- 23
- Pages
- 460-472
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC10792694
- OpenAlex
- W4389988233
- Cited by
- 14
- References
- 99
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 3
- FWCI
- 1.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- National Natural Science Foundation of China (32070665)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- data scarcity, transfer learning, structure-based prediction, sequence-based prediction, ensemble learning, random forest regression, multilayer perceptron, protein-protein interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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