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]
- alphaXiv
- arXiv
- CatalyzeX
- Critical Mass
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv
- maximum common edge subgraph
- ScienceCast
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