{"slug": "nearest-neighbour-baselines-for-fingerprint-prediction-from-ms-ms-spectra-under", "title": "Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions", "summary": "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.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 30 Sep 2026]\n\n# Title:Nearest-neighbour baselines for fingerprint prediction from MS/MS spectra under different assumptions\n\n[View PDF](https://arxiv.org/pdf/2610.02249)\n\n[HTML (experimental)](https://arxiv.org/html/2610.02249v1)\n\nAbstract: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.\n    \n\n## Submission history\n\nFrom: Ling Min Serena Khoo [\n[view email](https://arxiv.org/show-email/01818007/2610.02249)]\n\n**[v1]** Wed, 30 Sep 2026 18:41:00 UTC (12 KB)\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth 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.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/nearest-neighbour-baselines-for-fingerprint-prediction-from-ms-ms-spectra-under", "canonical_source": "https://arxiv.org/abs/2610.02249", "published_at": "2026-10-05 04:00:00+00:00", "updated_at": "2026-10-05 04:12:18.912060+00:00", "lang": "en", "topics": ["machine-learning", "ai-research"], "entities": ["Ling Min Serena Khoo", "arXiv", "Khoo and Barzilay", "Liu et al.", "Gupta et al."], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/nearest-neighbour-baselines-for-fingerprint-prediction-from-ms-ms-spectra-under", "markdown": "https://wpnews.pro/news/nearest-neighbour-baselines-for-fingerprint-prediction-from-ms-ms-spectra-under.md", "text": "https://wpnews.pro/news/nearest-neighbour-baselines-for-fingerprint-prediction-from-ms-ms-spectra-under.txt", "jsonld": "https://wpnews.pro/news/nearest-neighbour-baselines-for-fingerprint-prediction-from-ms-ms-spectra-under.jsonld"}}