Datamol Conformers Module Reference

The datamol.conformers module provides tools for generating and analyzing 3D molecular conformations.

Conformer Generation

dm.conformers.generate(mol, n_confs=None, rms_cutoff=None, minimize_energy=True, method='ETKDGv3', add_hs=True, ...)

Generate 3D molecular conformers.

• Parameters:
  • mol: Input molecule
  • n_confs: Number of conformers to generate (auto-determined based on rotatable bonds if None)
  • rms_cutoff: RMS threshold in Ångströms for filtering similar conformers (removes duplicates)
  • minimize_energy: Apply UFF energy minimization (default: True)
  • method: Embedding method - options:
    • 'ETDG' - Experimental Torsion Distance Geometry
    • 'ETKDG' - ETDG with additional basic knowledge
    • 'ETKDGv2' - Enhanced version 2
    • 'ETKDGv3' - Enhanced version 3 (default, recommended)
  • add_hs: Add hydrogens before embedding (default: True, critical for quality)
  • random_seed: Set for reproducibility
• Returns: Molecule with embedded conformers
• Example:
  pythonCopy

  mol = dm.to_mol("CCO")
  mol_3d = dm.conformers.generate(mol, n_confs=10, rms_cutoff=0.5)
  conformers = mol_3d.GetConformers()  # Access all conformers
  

Conformer Clustering

dm.conformers.cluster(mol, rms_cutoff=1.0, already_aligned=False, centroids=False)

Group conformers by RMS distance.

• Parameters:
  • rms_cutoff: Clustering threshold in Ångströms (default: 1.0)
  • already_aligned: Whether conformers are pre-aligned
  • centroids: Return centroid conformers (True) or cluster groups (False)
• Returns: Cluster information or centroid conformers
• Use case: Identify distinct conformational families

dm.conformers.return_centroids(mol, conf_clusters, centroids=True)

Extract representative conformers from clusters.

• Parameters:
  • conf_clusters: Sequence of cluster indices from cluster()
  • centroids: Return single molecule (True) or list of molecules (False)
• Returns: Centroid conformer(s)

Conformer Analysis

dm.conformers.rmsd(mol)

Calculate pairwise RMSD matrix across all conformers.

• Requirements: Minimum 2 conformers
• Returns: NxN matrix of RMSD values
• Use case: Quantify conformer diversity

dm.conformers.sasa(mol, n_jobs=1, ...)

Calculate Solvent Accessible Surface Area (SASA) using FreeSASA.

• Parameters:
  • n_jobs: Parallelization for multiple conformers
• Returns: Array of SASA values (one per conformer)
• Storage: Values stored in each conformer as property 'rdkit_free_sasa'
• Example:
  pythonCopy

  sasa_values = dm.conformers.sasa(mol_3d)
  # Or access from conformer properties
  conf = mol_3d.GetConformer(0)
  sasa = conf.GetDoubleProp('rdkit_free_sasa')
  

Low-Level Conformer Manipulation

dm.conformers.center_of_mass(mol, conf_id=-1, use_atoms=True, round_coord=None)

Calculate molecular center.

• Parameters:
  • conf_id: Conformer index (-1 for first conformer)
  • use_atoms: Use atomic masses (True) or geometric center (False)
  • round_coord: Decimal precision for rounding
• Returns: 3D coordinates of center
• Use case: Centering molecules for visualization or alignment

dm.conformers.get_coords(mol, conf_id=-1)

Retrieve atomic coordinates from a conformer.

• Returns: Nx3 numpy array of atomic positions
• Example:
  pythonCopy

  positions = dm.conformers.get_coords(mol_3d, conf_id=0)
  # positions.shape: (num_atoms, 3)
  

dm.conformers.translate(mol, conf_id=-1, transform_matrix=None)

Reposition conformer using transformation matrix.

• Modification: Operates in-place
• Use case: Aligning or repositioning molecules

Workflow Example

import datamol as dm

# 1. Create molecule and generate conformers
mol = dm.to_mol("CC(C)CCO")  # Isopentanol
mol_3d = dm.conformers.generate(
    mol,
    n_confs=50,           # Generate 50 initial conformers
    rms_cutoff=0.5,       # Filter similar conformers
    minimize_energy=True   # Minimize energy
)

# 2. Analyze conformers
n_conformers = mol_3d.GetNumConformers()
print(f"Generated {n_conformers} unique conformers")

# 3. Calculate SASA
sasa_values = dm.conformers.sasa(mol_3d)

# 4. Cluster conformers
clusters = dm.conformers.cluster(mol_3d, rms_cutoff=1.0, centroids=False)

# 5. Get representative conformers
centroids = dm.conformers.return_centroids(mol_3d, clusters)

# 6. Access 3D coordinates
coords = dm.conformers.get_coords(mol_3d, conf_id=0)

Key Concepts

• Distance Geometry: Method for generating 3D structures from connectivity information
• ETKDG: Uses experimental torsion angle preferences and additional chemical knowledge
• RMS Cutoff: Lower values = more unique conformers; higher values = fewer, more distinct conformers
• Energy Minimization: Relaxes structures to nearest local energy minimum
• Hydrogens: Critical for accurate 3D geometry - always include during embedding