针对肽类药物渗透性预测难题，提出分长度建模的多尺度融合框架。
LengthLogD: A Length-Stratified Ensemble Framework for Enhanced Peptide Lipophilicity Prediction via Multi-Scale Feature Integration
- 按肽链长度分层建模，融合原子、结构、拓扑三类特征
- 长肽预测误差降低34.7%，长肽R²达0.882，显著优于传统方法
- 特别适合优化长肽类候选药物，对药物研发有实用价值
肽类化合物因其高靶点亲和力与低毒性，在药物开发中具有巨大潜力，但其低膜通透性限制了应用。分子量与肽链长度显著影响肽类的logD值，进而决定其跨膜能力。然而，由于序列、结构与电离状态间的复杂交互，准确预测肽类logD仍具挑战。本研究提出LengthLogD框架，通过分子长度分层构建专用模型，并创新性整合多尺度分子表征。在三个层级构建特征空间：原子级（10个分子描述符）、结构级（1024位Morgan指纹）、拓扑级（3个图基特征，包括威纳指数），并通过分层集成学习优化。为长肽设计自适应权重分配机制，显著提升泛化能力。实验结果表明，短肽（R²=0.855）、中肽（R²=0.816）、长肽（R²=0.882）均表现优异，长肽预测误差较传统单模型降低34.7%。消融实验证实：1）分层策略贡献41.2%性能提升；2）拓扑特征占预测重要性的28.5%。相较现有先进模型，本方法在保持短肽预测精度的同时，使长肽R²提升25.7%。该研究为肽类药物研发提供高精度logD预测工具，尤其在优化长肽先导化合物方面具有独特价值。
原文摘要 · Abstract (English)
Peptide compounds demonstrate considerable potential as therapeutic agents due to their high target affinity and low toxicity, yet their drug development is constrained by their low membrane permeability. Molecular weight and peptide length have significant effects on the logD of peptides, which in turn influences their ability to cross biological membranes. However, accurate prediction of peptide logD remains challenging due to the complex interplay between sequence, structure, and ionization states. This study introduces LengthLogD, a predictive framework that establishes specialized models through molecular length stratification while innovatively integrating multi-scale molecular representations. We constructed feature spaces across three hierarchical levels: atomic (10 molecular descriptors), structural (1024-bit Morgan fingerprints), and topological (3 graph-based features including Wiener index), optimized through stratified ensemble learning. An adaptive weight allocation mechanism specifically developed for long peptides significantly enhances model generalizability. Experimental results demonstrate superior performance across all categories: short peptides (R^2=0.855), medium peptides (R^2=0.816), and long peptides (R^2=0.882), with a 34.7% reduction in prediction error for long peptides compared to conventional single-model approaches. Ablation studies confirm: 1) The length-stratified strategy contributes 41.2% to performance improvement; 2) Topological features account for 28.5% of predictive importance. Compared to state-of-the-art models, our method maintains short peptide prediction accuracy while achieving a 25.7% increase in the coefficient of determination (R^2) for long peptides. This research provides a precise logD prediction tool for peptide drug development, particularly demonstrating unique value in optimizing long peptide lead compounds.
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