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

A September 30, 2026 arXiv report by Ling Min Serena Khoo systematically compares several nearest-neighbour retrieval variants for molecular fingerprint prediction from MS/MS spectra, showing how differing assumptions about information available at inference affect performance. The work follows prior results (Khoo and Barzilay, 2026; Liu et al., 2026; Gupta et al., 2026) showing nearest-neighbour retrieval matches or outperforms current deep learning models, and aims to establish stricter baselines for more rigorous benchmarking.

Computer Science Machine Learning Submitted on 30 Sep 2026 Title:Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions View PDF https://arxiv.org/pdf/2610.02249 HTML experimental https://arxiv.org/html/2610.02249v1 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. Submission history From: Ling Min Serena Khoo view email https://arxiv.org/show-email/01818007/2610.02249 v1 Wed, 30 Sep 2026 18:41:00 UTC 12 KB Current browse context: cs.LG References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender IArxiv Recommender What is IArxiv? https://iarxiv.org/about arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .