Platform wheels for RDKit - a comprehensive cheminformatics and machine-learning library with Python bindings
89
{
"context": "This criteria evaluates how well the engineer uses RDKit's core functionality for 3D conformer generation from SMILES strings. It focuses on proper usage of RDKit's molecular parsing and conformer embedding capabilities.",
"type": "weighted_checklist",
"checklist": [
{
"name": "SMILES parsing",
"description": "Uses Chem.MolFromSmiles() to parse SMILES strings into molecule objects",
"max_score": 20
},
{
"name": "Conformer embedding",
"description": "Uses AllChem.EmbedMolecule() to generate a single 3D conformer with distance geometry",
"max_score": 30
},
{
"name": "Invalid input handling",
"description": "Checks for None returned by MolFromSmiles() and handles failed conformer generation (when EmbedMolecule() returns -1)",
"max_score": 20
},
{
"name": "Stereochemistry preservation",
"description": "Uses appropriate parameters in EmbedMolecule() to preserve stereochemistry from input SMILES (e.g., useRandomCoords=False or similar chirality handling)",
"max_score": 15
},
{
"name": "Module imports",
"description": "Correctly imports necessary RDKit modules (rdkit.Chem and rdkit.Chem.AllChem)",
"max_score": 15
}
]
}Install with Tessl CLI
npx tessl i tessl/pypi-rdkitevals
scenario-1
scenario-2
scenario-3
scenario-4
scenario-5
scenario-6
scenario-7
scenario-8
scenario-9
scenario-10