Researchers have identified significant failures in common evaluation methods for molecular property models, particularly those used in ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) prediction. A new 'structural-frontier' split method, which reserves the most chemically remote scaffold groups, revealed that standard scaffold-based splits can hide substantial performance degradation. This frontier split inflated primary error by a median of 87.0% compared to a standard control, indicating that current evaluation practices may overestimate model reliability for novel chemical structures. AI
IMPACT Highlights the need for more robust evaluation metrics in cheminformatics to ensure reliable predictions for novel molecular structures.
RANK_REASON The item is a research paper detailing a new evaluation methodology for machine learning models in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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