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Research reveals flaws in molecular machine learning splitting conventions

A new research paper titled "Critical Mass: Structure-Disjoint Splits Drift With the Libraries They Are Built On" highlights a significant issue in molecular machine learning. The study reveals that the common practice of using structure-disjoint splits for evaluating out-of-distribution performance is flawed. This method, which relies on a fixed distance threshold, was established on a smaller dataset and does not scale effectively with current, much larger public libraries. The research proposes an optimized approach that significantly reduces computational complexity while maintaining data integrity, uncovering that the current splitting convention leads to benchmarks that conflate structural novelty with extrapolation to more complex chemical structures. AI

IMPACT Highlights potential inaccuracies in current benchmarks for molecular machine learning, suggesting a need for revised evaluation methodologies.

RANK_REASON The cluster contains a research paper detailing a new finding and proposed method in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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Research reveals flaws in molecular machine learning splitting conventions

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The cluster contains a research paper detailing a new finding and proposed method in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tomo Oga, Devesh Shah, Cailum M. K. Stienstra, Gabriel Asher, Antonio H. de O. Fonseca, Niall O'Connor, Michael Widrich ·

    Critical Mass: Structure-Disjoint Splits Drift With the Libraries They Are Built On

    arXiv:2610.12694v1 Announce Type: new Abstract: Molecular machine learning evaluates out-of-distribution performance with structure disjoint splits, a construction rule fixed once at the scale its library happened to have: threshold a pairwise distance, then assign whole connecte…