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Modeling Method Based on Traditional Machine Learning and Graph Neural Network for Interpretable Prediction of Short
Jing Li1, Yutian Gu1, Qianyu Guo1
1Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, Qianjin road 2699, Changchun 130012, China.
The Journal of Physical Chemistry. B
|April 20, 2026
Summary
Predicting peptide lipophilicity (logD) is vital for drug development. This study introduces graph neural networks (GNNs) for accurate logD prediction in short peptides, offering an interpretable tool for designing peptide therapeutics.
Area of Science:
- Computational Chemistry
- Medicinal Chemistry
- Drug Discovery
Background:
- Accurate prediction of octanol/water distribution coefficient (logD) is essential for assessing drug candidate lipophilicity and ADMET profiles.
- The growing field of peptide therapeutics necessitates reliable logD prediction methods for short peptides (2-6 residues).
Purpose of the Study:
- To develop and compare traditional machine learning and graph neural network (GNN) models for predicting the logD of short peptides.
- To identify key molecular features influencing peptide logD and provide an interpretable prediction tool.
Main Methods:
- Utilized two dedicated peptide logD datasets.
- Developed machine learning models using molecular fingerprints and descriptors.
- Implemented and evaluated five advanced GNN architectures, including WLN and WeaveGNN.
- Constructed consensus models by combining top-performing traditional and GNN models.
Main Results:
- The WLN GNN model achieved the highest predictive performance among individual GNNs (R²=0.907).
- A consensus model combining SVM, WLN, and WeaveGNN demonstrated superior accuracy (R²=0.923, RMSE=0.343, MAE=0.241).
- Identified key molecular features influencing logD, such as MolLogP, fr_amide, and MaxEStateIndex.
Conclusions:
- Graph neural networks offer a powerful approach for predicting short peptide logD.
- The developed consensus model provides an accurate and interpretable tool for peptide-based drug design.
- This study is the first to apply GNNs to short peptide logD prediction, offering valuable insights for lipophilic peptide therapeutic development.
