alphafold
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) P…
Maintainer FreedomIntelligence · Last updated April 1, 2026
Calculates molecular descriptors and fingerprints using RDKit. Computes Morgan fingerprints (ECFP), MACCS keys, Lipinski properties, QED drug-likeness, TPSA, and 3D conformer descriptors. Use when featurizing molecules for machine learning or filtering by drug-likeness criteria.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-molecular-descriptors
Skill Snapshot
Source Doc
Goal: Generate circular fingerprints that encode local chemical environments for similarity searching and ML models.
Approach: Use GetMorganFingerprintAsBitVect with a chosen radius (2 for ECFP4, 3 for ECFP6) and bit length, optionally including chirality information.
from rdkit import Chem
from rdkit.Chem import AllChem
mol = Chem.MolFromSmiles('CCO')
## With stereochemistry information
ecfp4_chiral = AllChem.GetMorganFingerprintAsBitVect(
mol, radius=2, nBits=2048, useChirality=True
)
## As count vector (for some ML methods)
ecfp4_counts = AllChem.GetMorganFingerprint(mol, radius=2)
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