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+# Datamol Descriptors and Visualization Reference
+
+## Descriptors Module (`datamol.descriptors`)
+
+The descriptors module provides tools for computing molecular properties and descriptors.
+
+### Specialized Descriptor Functions
+
+#### `dm.descriptors.n_aromatic_atoms(mol)`
+Calculate the number of aromatic atoms.
+- **Returns**: Integer count
+- **Use case**: Aromaticity analysis
+
+#### `dm.descriptors.n_aromatic_atoms_proportion(mol)`
+Calculate ratio of aromatic atoms to total heavy atoms.
+- **Returns**: Float between 0 and 1
+- **Use case**: Quantifying aromatic character
+
+#### `dm.descriptors.n_charged_atoms(mol)`
+Count atoms with nonzero formal charge.
+- **Returns**: Integer count
+- **Use case**: Charge distribution analysis
+
+#### `dm.descriptors.n_rigid_bonds(mol)`
+Count non-rotatable bonds (neither single bonds nor ring bonds).
+- **Returns**: Integer count
+- **Use case**: Molecular flexibility assessment
+
+#### `dm.descriptors.n_stereo_centers(mol)`
+Count stereogenic centers (chiral centers).
+- **Returns**: Integer count
+- **Use case**: Stereochemistry analysis
+
+#### `dm.descriptors.n_stereo_centers_unspecified(mol)`
+Count stereocenters lacking stereochemical specification.
+- **Returns**: Integer count
+- **Use case**: Identifying incomplete stereochemistry
+
+### Batch Descriptor Computation
+
+#### `dm.descriptors.compute_many_descriptors(mol, properties_fn=None, add_properties=True)`
+Compute multiple molecular properties for a single molecule.
+- **Parameters**:
+  - `properties_fn`: Custom list of descriptor functions
+  - `add_properties`: Include additional computed properties
+- **Returns**: Dictionary of descriptor name → value pairs
+- **Default descriptors include**:
+  - Molecular weight, LogP, number of H-bond donors/acceptors
+  - Aromatic atoms, stereocenters, rotatable bonds
+  - TPSA (Topological Polar Surface Area)
+  - Ring count, heteroatom count
+- **Example**:
+  ```python
+  mol = dm.to_mol("CCO")
+  descriptors = dm.descriptors.compute_many_descriptors(mol)
+  # Returns: {'mw': 46.07, 'logp': -0.03, 'hbd': 1, 'hba': 1, ...}
+  ```
+
+#### `dm.descriptors.batch_compute_many_descriptors(mols, properties_fn=None, add_properties=True, n_jobs=1, batch_size=None, progress=False)`
+Compute descriptors for multiple molecules in parallel.
+- **Parameters**:
+  - `mols`: List of molecules
+  - `n_jobs`: Number of parallel jobs (-1 for all cores)
+  - `batch_size`: Chunk size for parallel processing
+  - `progress`: Show progress bar
+- **Returns**: Pandas DataFrame with one row per molecule
+- **Example**:
+  ```python
+  mols = [dm.to_mol(smi) for smi in smiles_list]
+  df = dm.descriptors.batch_compute_many_descriptors(
+      mols,
+      n_jobs=-1,
+      progress=True
+  )
+  ```
+
+### RDKit Descriptor Access
+
+#### `dm.descriptors.any_rdkit_descriptor(name)`
+Retrieve any descriptor function from RDKit by name.
+- **Parameters**: `name` - Descriptor function name (e.g., 'MolWt', 'TPSA')
+- **Returns**: RDKit descriptor function
+- **Available descriptors**: From `rdkit.Chem.Descriptors` and `rdkit.Chem.rdMolDescriptors`
+- **Example**:
+  ```python
+  tpsa_fn = dm.descriptors.any_rdkit_descriptor('TPSA')
+  tpsa_value = tpsa_fn(mol)
+  ```
+
+### Common Use Cases
+
+**Drug-likeness Filtering (Lipinski's Rule of Five)**:
+```python
+descriptors = dm.descriptors.compute_many_descriptors(mol)
+is_druglike = (
+    descriptors['mw'] <= 500 and
+    descriptors['logp'] <= 5 and
+    descriptors['hbd'] <= 5 and
+    descriptors['hba'] <= 10
+)
+```
+
+**ADME Property Analysis**:
+```python
+df = dm.descriptors.batch_compute_many_descriptors(compound_library)
+# Filter by TPSA for blood-brain barrier penetration
+bbb_candidates = df[df['tpsa'] < 90]
+```
+
+---
+
+## Visualization Module (`datamol.viz`)
+
+The viz module provides tools for rendering molecules and conformers as images.
+
+### Main Visualization Function
+
+#### `dm.viz.to_image(mols, legends=None, n_cols=4, use_svg=False, mol_size=(200, 200), highlight_atom=None, highlight_bond=None, outfile=None, max_mols=None, copy=True, indices=False, ...)`
+Generate image grid from molecules.
+- **Parameters**:
+  - `mols`: Single molecule or list of molecules
+  - `legends`: String or list of strings as labels (one per molecule)
+  - `n_cols`: Number of molecules per row (default: 4)
+  - `use_svg`: Output SVG format (True) or PNG (False, default)
+  - `mol_size`: Tuple (width, height) or single int for square images
+  - `highlight_atom`: Atom indices to highlight (list or dict)
+  - `highlight_bond`: Bond indices to highlight (list or dict)
+  - `outfile`: Save path (local or remote, supports fsspec)
+  - `max_mols`: Maximum number of molecules to display
+  - `indices`: Draw atom indices on structures (default: False)
+  - `align`: Align molecules using MCS (Maximum Common Substructure)
+- **Returns**: Image object (can be displayed in Jupyter) or saves to file
+- **Example**:
+  ```python
+  # Basic grid
+  dm.viz.to_image(mols[:10], legends=[dm.to_smiles(m) for m in mols[:10]])
+
+  # Save to file
+  dm.viz.to_image(mols, outfile="molecules.png", n_cols=5)
+
+  # Highlight substructure
+  dm.viz.to_image(mol, highlight_atom=[0, 1, 2], highlight_bond=[0, 1])
+
+  # Aligned visualization
+  dm.viz.to_image(mols, align=True, legends=activity_labels)
+  ```
+
+### Conformer Visualization
+
+#### `dm.viz.conformers(mol, n_confs=None, align_conf=True, n_cols=3, sync_views=True, remove_hs=True, ...)`
+Display multiple conformers in grid layout.
+- **Parameters**:
+  - `mol`: Molecule with embedded conformers
+  - `n_confs`: Number or list of conformer indices to display (None = all)
+  - `align_conf`: Align conformers for comparison (default: True)
+  - `n_cols`: Grid columns (default: 3)
+  - `sync_views`: Synchronize 3D views when interactive (default: True)
+  - `remove_hs`: Remove hydrogens for clarity (default: True)
+- **Returns**: Grid of conformer visualizations
+- **Use case**: Comparing conformational diversity
+- **Example**:
+  ```python
+  mol_3d = dm.conformers.generate(mol, n_confs=20)
+  dm.viz.conformers(mol_3d, n_confs=10, align_conf=True)
+  ```
+
+### Circle Grid Visualization
+
+#### `dm.viz.circle_grid(center_mol, circle_mols, mol_size=200, circle_margin=50, act_mapper=None, ...)`
+Create concentric ring visualization with central molecule.
+- **Parameters**:
+  - `center_mol`: Molecule at center
+  - `circle_mols`: List of molecule lists (one list per ring)
+  - `mol_size`: Image size per molecule
+  - `circle_margin`: Spacing between rings (default: 50)
+  - `act_mapper`: Activity mapping dictionary for color-coding
+- **Returns**: Circular grid image
+- **Use case**: Visualizing molecular neighborhoods, SAR analysis, similarity networks
+- **Example**:
+  ```python
+  # Show a reference molecule surrounded by similar compounds
+  dm.viz.circle_grid(
+      center_mol=reference,
+      circle_mols=[nearest_neighbors, second_tier]
+  )
+  ```
+
+### Visualization Best Practices
+
+1. **Use legends for clarity**: Always label molecules with SMILES, IDs, or activity values
+2. **Align related molecules**: Use `align=True` in `to_image()` for SAR analysis
+3. **Adjust grid size**: Set `n_cols` based on molecule count and display width
+4. **Use SVG for publications**: Set `use_svg=True` for scalable vector graphics
+5. **Highlight substructures**: Use `highlight_atom` and `highlight_bond` to emphasize features
+6. **Save large grids**: Use `outfile` parameter to save rather than display in memory