---
title: Descriptors & Property Checks
description: >-
  Calculate molecular descriptors (MW, logP, TPSA, HBA/HBD) and run
  drug-likeness checks with RDKit.
---

Descriptors let you profile a molecule's physicochemical properties — molecular weight, logP, TPSA, hydrogen-bond donors and acceptors — and gate compounds through drug-likeness checks before expensive steps. The property bounds you set in your QC gate are what most affect how much of your set passes through to conformer generation or docking.

## Common descriptors

- Molecular weight: `Descriptors.MolWt(mol)`
- logP: `Descriptors.MolLogP(mol)`
- TPSA: `Descriptors.TPSA(mol)`
- HBA/HBD: `Lipinski.NumHAcceptors`, `Lipinski.NumHDonors`
- Rotatable bonds: `Lipinski.NumRotatableBonds`

```python
from rdkit import Chem
from rdkit.Chem import Descriptors, Lipinski

mol = Chem.MolFromSmiles("CC(=O)Oc1ccccc1C(=O)O")

summary = {
    "mw": Descriptors.MolWt(mol),
    "logp": Descriptors.MolLogP(mol),
    "tpsa": Descriptors.TPSA(mol),
    "hba": Lipinski.NumHAcceptors(mol),
    "hbd": Lipinski.NumHDonors(mol),
    "rot_bonds": Lipinski.NumRotatableBonds(mol),
}
print(summary)
```

## QC gates

```python
def passes_qc(mol) -> bool:
    if mol is None:
        return False
    mw = Descriptors.MolWt(mol)
    logp = Descriptors.MolLogP(mol)
    rb = Lipinski.NumRotatableBonds(mol)
    if not (150 <= mw <= 600):
        return False
    if not (-1.5 <= logp <= 5.5):
        return False
    if rb > 10:
        return False
    return True
```

Apply `passes_qc` before expensive steps (conformer generation, docking) to save
time.

## Batch workflow

- Parse SMILES → run QC → write passing molecules to CSV/SDF.
- Log counts for pass/fail buckets to spot over-filtering.
- For large sets, run QC in chunks and stream outputs to avoid high memory use.