Editing AI in Natural Sciences (section)

== <span style="color: #FFFFFF;">Evaluating</span> ==
Evaluating AI in the natural sciences requires domain-specific benchmarks and scientific validation:

'''Structure prediction''': CASP (Critical Assessment of Protein Structure Prediction) — the biennial competition that benchmarks structure prediction methods against experimental structures. AlphaFold 2 effectively solved CASP14. For blind prediction: CAMEO (Continuous Automated Model Evaluation) provides weekly benchmarking.

'''Molecular property prediction''': MoleculeNet provides standardized benchmarks for toxicity (Tox21), drug properties (ADMET), and quantum mechanics (QM9). OGB-LSC (Large Scale Challenge) includes large-scale molecular benchmarks.

'''Weather/climate AI''': WeatherBench and WeatherBench2 benchmark global weather forecasting. GraphCast's 10-day forecast quality is now evaluated against ECMWF's state-of-the-art numerical model.

'''Scientific validity beyond metrics''': The key question is not just "does it score well on the benchmark?" but "does it produce scientifically valid results that advance understanding?" This requires wet-lab validation of AI-predicted molecules, experimental verification of AI-predicted structures, and peer review.

Expert practitioners in AI for science always include domain experts who can detect physically or chemically nonsensical outputs that would fool automated metrics.
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