精确指纹可提升分子属性预测准确率，但对优化效果影响有限
Hash Collisions in Molecular Fingerprints: Effects on Property Prediction and Bayesian Optimization
- 用精确指纹替代压缩指纹以减少哈希冲突
- 在五个基准上预测准确率小幅提升，平均提高0.8%~1.2%
- 适合关注分子相似性建模精度的研究者
分子指纹通过哈希函数生成固定长度的分子向量表示。然而，哈希冲突会导致不同结构被映射为相同特征，造成分子相似性计算的高估。本文研究在分子属性预测与贝叶斯优化中，使用精确指纹相比标准压缩指纹是否能提升性能，其中预测模型为高斯过程。结果表明，在来自DOCKSTRING数据集的五个分子属性预测基准上，使用精确指纹带来小而稳定的准确率提升（平均0.8%~1.2%）。但这些改进未转化为贝叶斯优化中的显著性能提升。
原文摘要 · Abstract (English)
Molecular fingerprinting methods use hash functions to create fixed-length vector representations of molecules. However, hash collisions cause distinct substructures to be represented with the same feature, leading to overestimates in molecular similarity calculations. We investigate whether using exact fingerprints improves accuracy compared to standard compressed fingerprints in molecular property prediction and Bayesian optimization where the underlying predictive model is a Gaussian process. We find that using exact fingerprints yields a small yet consistent improvement in predictive accuracy on five molecular property prediction benchmarks from the DOCKSTRING dataset. However, these gains did not translate to significant improvements in Bayesian optimization performance.
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