Bio Conformer Generation

Version Compatibility

Reference examples tested with: RDKit 2024.09+, xtb 6.7+, CREST 3.0+, OpenMM 8.1+ for follow-up MD.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: xtb --version; crest --version

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Conformer Generation

Generate 3D conformer ensembles for molecules from 2D structures. The choice of method depends on molecule size, flexibility, and downstream use: ETKDG / ETKDGv3 (Riniker & Landrum 2015 J Chem Inf Model 55:2562-2574; knowledge-enhanced distance geometry) is the modern default for drug-like molecules, MMFF94/UFF for fast energy minimization, CREST + GFN2-xTB for high-accuracy semi-empirical sampling of macrocycles and peptides. A single conformer is rarely sufficient: descriptor variance across the ensemble can exceed the descriptor signal, and docking pose accuracy degrades if the starting conformer is non-bioactive.

For docking pose validation, see chemoinformatics/pose-validation. For free-energy methods (which require ensemble sampling), see chemoinformatics/free-energy-calculations.

Conformer Method Taxonomy

Decision: For drug-like molecules (<500 Da, <8 rotatable bonds), **ETKDGv3 + MMFF94** with 20-100 conformers is the modern default. For macrocycles, peptides, or molecules with >12 rotatable bonds, CREST + GFN2-xTB captures the conformational diversity. For ML-scale (>1M molecules), GeoMol trades accuracy for speed.

Decision Tree by Scenario

ETKDGv3 (Modern Default)

ETKDGv3 (Riniker & Landrum 2015) incorporates experimental torsion preferences into distance geometry: starts from random embeddings, refines by satisfying experimentally-derived bond, angle, and torsion preferences.

Goal: Generate an ensemble of 3D conformers from a SMILES with the modern default embedding algorithm.

Approach: Add explicit hydrogens, configure ETKDGv3 params (random seed, max attempts, random coords), and embed multiple conformers via EmbedMultipleConfs.

from rdkit import Chem
from rdkit.Chem import AllChem

def gen_conformers(smiles, n_conf=20, seed=42):
    mol = Chem.MolFromSmiles(smiles)
    mol = Chem.AddHs(mol)
    params = AllChem.ETKDGv3()
    params.randomSeed = seed
    params.useRandomCoords = True
    params.maxAttempts = 1000
    ids = AllChem.EmbedMultipleConfs(mol, numConfs=n_conf, params=params)
    return mol, list(ids)

useRandomCoords=True improves convergence for macrocycles and heavily-rotated molecules. maxAttempts=1000 handles difficult embeddings.

Force-Field Optimization

After embedding, minimize each conformer to a local minimum.

Goal: Reduce strain in each embedded conformer to a stable local minimum and record the resulting energies.

Approach: Build MMFF94s force-field parameters, minimize each conformer in place, and collect energies; fall back to UFF when MMFF94 cannot parameterize the molecule.

def optimize_conformers(mol, conf_ids, force_field='mmff94'):
    energies = []
    if force_field == 'mmff94':
        mmff_props = AllChem.MMFFGetMoleculeProperties(mol, mmffVariant='MMFF94s')
        for cid in conf_ids:
            ff = AllChem.MMFFGetMoleculeForceField(mol, mmff_props, confId=cid)
            ff.Minimize()
            energies.append(ff.CalcEnergy())
    else:  # UFF fallback
        for cid in conf_ids:
            ff = AllChem.UFFGetMoleculeForceField(mol, confId=cid)
            ff.Minimize()
            energies.append(ff.CalcEnergy())
    return energies

MMFF94 vs MMFF94s: MMFF94s is the "standard" set with simpler aromatic nitrogen handling; preferred for most drug-like.

UFF (Universal Force Field): Lower quality but handles any element including transition metals. Use as fallback when MMFF94 cannot parameterize (uncommon elements, charged species).

RMSD Pruning

Remove near-duplicate conformers within a chosen RMSD cutoff to keep the ensemble diverse:

import numpy as np

def prune_conformers_rmsd(mol, conf_ids, rmsd_cutoff=0.5):
    n = len(conf_ids)
    keep = []
    for i, cid in enumerate(conf_ids):
        is_unique = True
        for kept_cid in keep:
            rmsd = AllChem.GetBestRMS(mol, mol, cid, kept_cid)
            if rmsd < rmsd_cutoff:
                is_unique = False
                break
        if is_unique:
            keep.append(cid)
    return keep

Typical RMSD cutoff (Source / Rationale):

Energy Window Filtering

Remove conformers above an energy cutoff (high-energy conformers are unlikely to be bioactive):

def filter_by_energy(mol, conf_ids, energies, window_kcal=10.0):
    min_e = min(energies)
    keep = []
    for cid, e in zip(conf_ids, energies):
        if e - min_e <= window_kcal:
            keep.append(cid)
    return keep

Window choice:

• 3 kcal/mol: very strict, only near-global-min conformers (FEP, MD setup)
• 5 kcal/mol: typical for 3D QSAR, pharmacophore
• 10 kcal/mol: typical for docking input (bioactive conformer may be higher)
• 25 kcal/mol: macrocycles, no filter (bioactive conformer can be high-energy when bound)

Macrocycle Handling

Macrocycles (>=12 atom rings) have distinct conformational issues: ETKDGv3 default knowledge base under-samples macrocycle torsions. Use macrocycle-specific torsion preferences:

from rdkit.Chem import AllChem

def macrocycle_conformers(smiles, n_conf=200, seed=42):
    mol = Chem.MolFromSmiles(smiles)
    mol = Chem.AddHs(mol)
    params = AllChem.ETKDGv3()
    params.randomSeed = seed
    params.useRandomCoords = True
    params.useMacrocycleTorsions = True
    params.useSmallRingTorsions = True
    params.maxAttempts = 5000
    ids = AllChem.EmbedMultipleConfs(mol, numConfs=n_conf, params=params)
    return mol, list(ids)

For pharmaceutical macrocycles (cyclosporine, paclitaxel, large peptides), CREST + GFN2-xTB is the gold standard.

CREST + GFN2-xTB for High-Quality Sampling

CREST (Grimme 2024) performs iterative meta-dynamics + GFN2-xTB optimization for conformer sampling.

Goal: Sample high-quality conformer ensembles for macrocycles, peptides, or molecules where ETKDGv3 + MMFF94 is inadequate.

Approach: Start from an RDKit-generated MMFF94-relaxed conformer, write to XYZ, and run CREST with GFN2-xTB driver to perform iterative meta-dynamics + reoptimization.

xtb mol.xyz --opt extreme
crest opt.xyz --gfn2 --T 12 -ewin 6

--gfn2: use GFN2-xTB (most accurate of GFN family for drug-like molecules). --gfn-ff: use GFN-FF (faster, less accurate). -ewin 6: 6 kcal/mol energy window above global min. -T 12: use 12 CPU threads.

Output: crest_conformers.xyz with sampled ensemble.

Workflow: Start from RDKit ETKDGv3 + MMFF94 (cheap initial structure) → save as XYZ → CREST refinement.

from rdkit import Chem
from rdkit.Chem import AllChem
import subprocess

def crest_workflow(smiles, out_dir='crest_out'):
    mol = Chem.MolFromSmiles(smiles)
    mol = Chem.AddHs(mol)
    AllChem.EmbedMolecule(mol, AllChem.ETKDGv3())
    AllChem.MMFFOptimizeMolecule(mol)

    xyz = Chem.MolToXYZBlock(mol)
    with open(f'{out_dir}/input.xyz', 'w') as f:
        f.write(xyz)
    subprocess.run(['crest', f'{out_dir}/input.xyz', '--gfn2', '-T', '12'],
                   cwd=out_dir, check=True)
    return f'{out_dir}/crest_conformers.xyz'

Boltzmann Averaging of Properties

For ensemble descriptors (3D shape, dipole moment, polar surface area in 3D), Boltzmann-weight by energy:

import numpy as np

def boltzmann_weights(energies, T=300.0):
    energies = np.array(energies)
    kt = 0.001987 * T  # kcal/mol at 300K
    rel = energies - energies.min()
    w = np.exp(-rel / kt)
    return w / w.sum()

def boltzmann_average(values, energies, T=300.0):
    w = boltzmann_weights(energies, T)
    return float(np.sum(np.array(values) * w))

For Boltzmann averaging, energies should be MMFF94 or higher quality. UFF energies are unreliable for Boltzmann weighting.

ML-Based Conformer Generation (GeoMol, TorsionNet)

For very large libraries (>1M compounds), classical methods become bottlenecks. ML-based methods generate conformers in <0.1s/mol on GPU:

# Pseudo-code for GeoMol-style ML conformer generation
# (Requires pre-trained model + dependencies)
# from geomol import generate_conformers
# conformers = generate_conformers(smiles, n_conformers=10)

Trade-off: ML methods (GeoMol, TorsionNet) match ETKDGv3 quality on drug-like molecules but extrapolate poorly outside training distribution (macrocycles, organometallics).

Per-Tool Failure Modes

ETKDGv3 -- failed embedding

Trigger: Macrocycle, highly constrained polycyclic, or sterically crowded molecule.

Mechanism: Distance geometry cannot find consistent 3D structure within max attempts.

Symptom: EmbedMolecule returns -1; EmbedMultipleConfs returns empty list.

Fix: Set useRandomCoords=True, increase maxAttempts to 5000+; for macrocycles, set useMacrocycleTorsions=True. As fallback, use CREST.

MMFF94 -- parameter missing

Trigger: Molecule contains element not parameterized (transition metals, certain S+ species).

Mechanism: MMFF94 only covers H, C, N, O, F, Si, P, S, Cl, Br, I + select cations.

Symptom: MMFFGetMoleculeProperties returns None; optimization silently no-ops.

Fix: Fall back to UFF; or for metals, use GFN2-xTB.

Conformer ensemble too small

Trigger: n_conf=10 for a flexible molecule (>5 rotatable bonds).

Mechanism: 10 conformers insufficient to sample conformational space; many minima missed.

Symptom: RMSD distribution narrow; descriptor variance underestimated.

Fix: Use n_conf = max(10, 5 * NumRotatableBonds + 10) heuristic (Hawkins 2017).

Single-conformer 3D descriptor

Trigger: Calculating 3D descriptors from a single conformer.

Mechanism: 3D descriptor variance across conformers can be 50%+ of mean.

Symptom: Same molecule produces different 3D descriptors on rerun.

Fix: Always compute descriptor over ensemble; report mean ± std, or Boltzmann-weighted mean.

CREST -- timeout on flexible molecule

Trigger: Cyclosporin or large peptide.

Mechanism: CREST metadynamics scales poorly with rotational complexity.

Symptom: Hours of CPU time per molecule; incomplete sampling.

Fix: Use --gfn-ff for faster initial sampling; reduce metadynamics time --mdtime 5 or skip metadyn with --noopt.

GFN2-xTB conformer reordering

Trigger: Comparing conformer energies between GFN2-xTB and DFT.

Mechanism: GFN2-xTB is parameterized for energies; relative conformer ordering can differ from DFT by 1-2 kcal/mol.

Symptom: "Wrong" conformer reported as global minimum vs DFT reference.

Fix: For high-stakes work, re-rank top GFN2-xTB conformers with DFT single-points (e.g., r2SCAN-3c).

Reconciliation: ETKDGv3 vs CREST

For ETKDGv3 ensembles, run CREST on a subset for benchmarking; if RMSD < 1A across methods, ETKDGv3 is adequate.

Common Errors

References

• Hawkins et al., J. Chem. Inf. Model. 50:572 (2010) -- OMEGA conformer sampling (commercial OpenEye tool; predecessor to ETKDG).
• Riniker & Landrum, J. Chem. Inf. Model. 55:2562-2574 (2015) -- ETKDG / ETKDGv3 with torsion preferences (the standard RDKit knowledge-distance-geometry embedder).
• Halgren, J. Comput. Chem. 17:490 (1996) -- MMFF94 force field.
• Rappe et al., J. Am. Chem. Soc. 114:10024 (1992) -- UFF.
• Pracht, Bohle, Grimme, J. Chem. Phys. 160:114110 (2024) -- CREST 3.0.
• Bannwarth et al., J. Chem. Theory Comput. 15:1652 (2019) -- GFN2-xTB.
• Ganea et al., NeurIPS (2021) -- GeoMol ML conformer generation.
• Hawkins, J. Chem. Inf. Model. 57:1747 (2017) -- conformer count heuristics.

Related Skills

• chemoinformatics/molecular-io - Parse molecules
• chemoinformatics/molecular-standardization - Standardize before embedding
• chemoinformatics/molecular-descriptors - 3D descriptors from ensembles
• chemoinformatics/shape-similarity - Multi-conformer 3D shape matching
• chemoinformatics/virtual-screening - Generate 3D ligands for docking
• chemoinformatics/free-energy-calculations - Sample conformers for MD setup
• chemoinformatics/pharmacophore-modeling - 3D pharmacophore from ensembles