CHEESE APISelections
Objectives
Rank eligible products with explicit metrics, models, directions, and references.
Objectives rank products that already satisfy constraints. A similarity objective names its reference, metric, and direction:
{
"type": "similarity",
"reference_id": "aspirin",
"metric_id": "rdkit.ecfp4_tanimoto",
"direction": "maximize"
}Initial similarity IDs are rdkit.ecfp4_tanimoto, cheese.shape, and
cheese.electrostatic. CHEESE objectives pin their model release and retain raw component scores.
Scientifically material choices are never inferred: shape versus electrostatic, logP versus logD,
scaffold method, or an ADMET model must be explicit.
Several references that jointly define one objective remain one selection. Independent result sets
for several inputs are a selection_batch run.