用于血脑屏障通透性预测的、几何信息感知的预训练分子图神经网络的参数高效微调
Geometry-Informed Parameter-Efficient Fine-Tuning of Pre-trained Molecular GNNs for Blood-Brain Barrier Permeability Prediction
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中文总结 AI 辅助
针对血脑屏障通透性预测任务，本文提出几何信息感知的PEFT框架BBBP-GeoPEFT，仅更新10.1%参数，在多数实验中取得有竞争力或提升的ROC-AUC与准确率。
中文摘要 AI 辅助
血脑屏障通透性（BBBP）预测是中枢神经系统药物发现中的关键筛选任务，需评估候选分子是否能穿过血脑屏障或应被阻止穿过。然而该任务仍具挑战性，原因在于数据集有限且类别不平衡，且对分子结构敏感。深度学习的最新进展已证实图神经网络（GNN）是分子表示学习的强大方法，而预训练分子GNN可为下游任务提供可迁移知识。但全量微调通常参数效率低下且易过拟合，现有参数高效微调（PEFT）方法主要适配节点特征或二维共价图，限制了其捕捉三维几何与二阶相互作用的能力。为解决这些局限，本文提出BBBP-GeoPEFT，一种用于预训练分子GNN的几何信息感知PEFT框架。BBBP-GeoPEFT基于分子构象体在多个截止值下构建基于距离的图及其对应的线图，以捕捉空间原子和二阶边相互作用。轻量辅助几何图编码器生成截止值特定的表示，通过节点式截止注意力和门控残差连接将其融入每个预训练层。该设计在保留预训练知识的同时，以少量可训练参数预算融入与通透性相关的几何信息。在整理后的BBBP数据集上的实验表明，BBBP-GeoPEFT与全量微调及代表性PEFT基线相比，取得了有竞争力的性能。在随机拆分和支架拆分两种设置下，BBBP-GeoPEFT在多数实验中取得了有竞争力或提升的ROC-AUC和准确率，且仅更新模型10.1%的参数。
英文摘要
Blood-brain barrier permeability (BBBP) prediction is a critical screening task in central nervous system drug discovery, where candidate molecules must be assessed for whether they can cross, or should be prevented from crossing, the blood-brain barrier. However, this task remains challenging because of limited, class-imbalanced datasets and sensitivity to molecular structure. Recent advances in deep learning have established graph neural networks (GNNs) as a powerful approach for molecular representation learning, while pre-trained molecular GNNs provide transferable knowledge for downstream tasks. However, full fine-tuning is often parameter-inefficient and prone to overfitting, whereas existing parameter-efficient fine-tuning (PEFT) methods mainly adapt node features or the two-dimensional covalent graph, limiting their ability to capture three-dimensional geometry and second-order interactions. To address these limitations, we propose BBBP-GeoPEFT, a geometry-informed PEFT framework for pre-trained molecular GNNs. BBBP-GeoPEFT constructs distance-based graphs at multiple cutoffs and their corresponding line graphs from molecular conformers to capture spatial atom and second-order edge interactions. Lightweight auxiliary geometric graph encoders generate cutoff-specific representations, which are incorporated into each pre-trained layer through node-wise cutoff attention and gated residual connections. This design preserves pre-trained knowledge while incorporating permeability-relevant geometric information with a small trainable-parameter budget. Experiments on a curated BBBP dataset show that BBBP-GeoPEFT achieves competitive performance compared with full fine-tuning and representative PEFT baselines. Under both random and scaffold splitting, BBBP-GeoPEFT achieves competitive or improved ROC-AUC and accuracy in most experiments while updating only 10.1% of the model parameters.
