Modeling Method Based on Traditional Machine Learning and Graph Neural Network for Interpretable Prediction of Short...

Accurate prediction of the octanol/water distribution coefficient (logD) is crucial for evaluating the lipophilicity, ADMET profiles, and potential of drug candidates. With the increasing interest in peptide-based therapeutics, there is a growing demand for reliable logD prediction methods specifically tailored to short peptides (2-6 residues). In this study, traditional machine learning and graph neural network (GNN) were employed to predict logD for short peptides at near-physiological pH levels (7.0-7.4). We took two dedicated peptide logD data sets and developed machine learning models using molecular fingerprints and descriptors. We also implemented five advanced GNN architectures. The WLN model achieved the best predictive performance in GNNs, with R² of 0.907, RMSE of 0.377, and MAE of 0.278, and outperformed the best traditional model, SVM with RFECV-processed descriptors, R² of 0.894, RMSE of 0.403, and MAE of 0.276). Additionally, we selected the first two models from each of these two types of models and constructed consensus models based on RMSE. Among them, the consensus model composed of SVM with RFECV-processed descriptors, WLN, and WeaveGNN performed the best. Its prediction accuracy (R² of 0.923, RMSE of 0.343, MAE of 0.241) has been significantly improved compared with the basic model that constitutes it. Then, we compared this consensus model with previous studies. Although we did not achieve better results, the highlight of our research lies in its interpretability. Our further analysis identified key molecular features influencing peptide logD, including MolLogP, fr_amide, and MaxEStateIndex descriptors, as well as peptide backbone and polar functional groups. This study is the first to apply the GNN method to the logD prediction task of short peptides and systematically compares the performance of different features and algorithms in this task. At the same time, we provide an interpretable logD prediction tool for short peptides to support the design of lipophilic peptide therapies. All source code we used are freely available at https://github.com/1756403aaa-sketch/logD.