Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions

基于不同假设下 MS/MS 质谱指纹预测的最近邻基准研究

Abstract: It has recently been shown that nearest-neighbour retrieval provides a strong baseline for molecular fingerprint prediction from MS/MS spectra, with several variants matching or outperforming current deep learning models (Khoo and Barzilay, 2026; Liu et al., 2026; Gupta et al., 2026).

摘要： 最近的研究表明，最近邻检索为基于 MS/MS 质谱的分子指纹预测提供了一个强有力的基准，其多种变体在性能上可以媲美甚至超越当前的深度学习模型（Khoo and Barzilay, 2026; Liu et al., 2026; Gupta et al., 2026）。

Importantly, “nearest neighbour” encompasses a family of retrieval methods that differ in the information assumed to be available at inference. In this report, we systematically compare several nearest-neighbour variants and show how these differing assumptions affect performance. Our goal is to establish stricter baselines that enable more rigorous benchmarking and better measure progress in this area.

重要的是，“最近邻”涵盖了一系列检索方法，这些方法在推理阶段所假设的可用信息上存在差异。在本报告中，我们系统地比较了多种最近邻变体，并展示了这些不同的假设如何影响性能。我们的目标是建立更严格的基准，从而实现更严谨的性能评估，并更好地衡量该领域的发展进展。