用键为中心的指纹提升血脑屏障预测，速度快效果好。
Bond-Centered Molecular Fingerprint Derivatives: A BBBP Dataset Study
- 提出新型键中心指纹BCFP，模拟图神经网络的键卷积机制。
- 结合ECFP后在BBBP数据集上AUROC和AUPRC均显著提升。
- 适合需要快速高效预测的药物分子筛选场景。
键中心指纹（BCFP）是扩展连接性指纹（ECFP）的互补型键中心替代方案。本文引入一种静态BCFP，其模拟了类似ChemProp的有向消息传递GNN中的键卷积，并使用快速随机森林模型在脑-血屏障穿透（BBBP）分类任务上进行评估。在分层交叉验证中，将ECFP与BCFP拼接后，无论在AUROC还是AUPRC上均持续优于单一特征，经土耳其HSD多重比较分析确认。在半径参数中，r=1表现最佳；而r=2未在相同测试下获得统计显著提升。我们进一步提出BCFP-Sort&Slice，一种简单特征组合方法，既保留了ECFP计数向量中的词外（OOV）信息，又支持无哈希拼接的紧凑表示。使用此类复合特征（包含键与原子特征），我们在BBBP评估中超越了MGTP模型表现。结果表明，轻量级键中心描述符能有效补充原子中心环状指纹，为BBBP预测提供强而快的基线。
原文摘要 · Abstract (English)
Bond Centered FingerPrint (BCFP) are a complementary, bond-centric alternative to Extended-Connectivity Fingerprints (ECFP). We introduce a static BCFP that mirrors the bond-convolution used by directed message-passing GNNs like ChemProp, and evaluate it with a fast rapid Random Forest model on Brain-Blood Barrier Penetration (BBBP) classification task. Across stratified cross-validation, concatenating ECFP with BCFP consistently improves AUROC and AUPRC over either descriptor alone, as confirmed by Turkey HSD multiple-comparison analysis. Among radii, r = 1 performs best; r = 2 does not yield statistically separable gains under the same test. We further propose BCFP-Sort&Slice, a simple feature-combination scheme that preserves the out-of-vocabulary (OOV) count information native to ECFP count vectors while enabling compact unhashed concatenation of BCFP variants. We also outperform the MGTP prediction on our BBBP evaluation, using such composite new features bond and atom features. These results show that lightweight, bond-centered descriptors can complement atom-centered circular fingerprints and provide strong, fast baselines for BBBP prediction.
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