对比多种方法在143分子多膜通透性预测中的表现，发现化学特征更适小样本研究。
A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset
- 用143个分子+6种膜的PAMPA数据，测试从线性回归到Transformer的多种模型
- 小样本下专家设计的理化性质描述符比深度学习表示更有效
- 揭示了预测性能与可解释性间的权衡，适合药企早期渗透性筛选
我们构建了一个独特的多任务数据集，包含143个药物及候选分子，每个分子在六种不同模型膜上通过体外平行人工膜渗透性实验（PAMPA）进行评估。基于该资源，系统评估了多种分子描述符和回归模型在预测被动膜渗透性方面的表现，涵盖从简单线性回归到现代预训练Transformer架构。特别关注预测性能与模型可解释性之间的权衡，揭示了机器学习方法带来的挑战。据我们所知，这是迄今最全面的多器官特异性PAMPA膜同时建模研究，为膜特异性渗透性特征提供了新见解。研究发现，在有限样本的渗透性研究中，专家设计的理化性质描述符比基于深度学习的表征更合适。
原文摘要 · Abstract (English)
We present a unique, multitask dataset comprising 143 drug and drug candidate molecules, each evaluated on in vitro, parallel artificial-membrane permeability assays (PAMPA) using six different model membranes. Using this resource, we systematically assess the effectiveness of various molecular descriptors and regression models in predicting passive membrane permeability. The studied models range from simple linear regression to a modern pre-trained transformer architecture. Particular attention is given to the trade-off between predictive performance and model interpretability, highlighting the challenges introduced by machine learning approaches. To our knowledge, this is the most comprehensive study on simultaneous modeling of multiple organ-specific PAMPA membranes to date, offering novel insights into membrane-specific permeability profiles. We found that expert-designed physico-chemical property descriptors are more fitting for a limited sample size permeabilty study than deep learning based representations.
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