Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions
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Computer Science > Machine Learning
Title:Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions
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). 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.
| Comments: | 6 pages, 2 figures |
| Subjects: | Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (stat.ML) |
| Cite as: | arXiv:2610.02249 [cs.LG] |
| (or arXiv:2610.02249v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02249
arXiv-issued DOI via DataCite
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Submission history
From: Ling Min Serena Khoo [view email][v1] Wed, 30 Sep 2026 18:41:00 UTC (12 KB)
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