用自注意力增强的GPS Transformer，小数据下预测血脑屏障通透性更准
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets
- 结合图位置编码与自注意力机制，提升小样本学习能力
- 在BBBP数据集上达78.8%的ROC-AUC，领先现有模型5.5个百分点
- 适合药物研发中数据少但需高精度预测的场景
血脑屏障（BBB）是保护大脑、调控物质进入中枢神经系统的关键屏障。评估候选药物的BBB通透性对精准药物靶向至关重要。然而，传统实验方法在大规模筛选中难以实施。因此，亟需开发计算预测方法。本文提出一种基于自注意力增强的GPS Transformer架构，专为低数据场景设计。该方法在BBBP数据集上的BBB通透性预测任务中达到78.8%的ROC-AUC，相较现有模型提升5.5个百分点，创下新纪录。实验表明，标准自注意力与GPS Transformer结合的效果优于其他注意力变体。
原文摘要 · Abstract (English)
The blood-brain barrier (BBB) serves as a protective barrier that separates the brain from the circulatory system, regulating the passage of substances into the central nervous system. Assessing the BBB permeability of potential drugs is crucial for effective drug targeting. However, traditional experimental methods for measuring BBB permeability are challenging and impractical for large-scale screening. Consequently, there is a need to develop computational approaches to predict BBB permeability. This paper proposes a GPS Transformer architecture augmented with Self Attention, designed to perform well in the low-data regime. The proposed approach achieved a state-of-the-art performance on the BBB permeability prediction task using the BBBP dataset, surpassing existing models. With a ROC-AUC of 78.8%, the approach sets a state-of-the-art by 5.5%. We demonstrate that standard Self Attention coupled with GPS transformer performs better than other variants of attention coupled with GPS Transformer.
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