Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
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 (Riniker & Landrum 2015) and its ETKDGv3 macrocycle update (Wang et al. 2020) are modern defaults for drug-like molecules, MMFF94/UFF provide fast energy minimization, and CREST + GFN2-xTB provide higher-cost semi-empirical sampling. A single conformer may be insufficient when the downstream result is conformation-sensitive; determine ensemble size by convergence of the downstream descriptor, alignment, or docking result.
For docking pose validation, see chemoinformatics/pose-validation. For free-energy methods (which require ensemble sampling), see chemoinformatics/free-energy-calculations.
Training distribution and implementation availability
MD sampling (OpenMM)
System/protocol-dependent
Dynamic sampling
Free energy, induced fit
Computational cost and convergence
Decision: Start drug-like organic molecules with ETKDGv3 and a parameter-checked MMFF94/MMFF94s optimization. Escalate difficult macrocycles, peptides, or highly flexible molecules to a validated CREST workflow when downstream convergence is inadequate. Benchmark ML generators on the intended chemistry before using them at scale.
Decision Tree by Scenario
Scenario
Starting method
Sampling and filtering decision
Single initial 3D structure
ETKDGv3 + checked force field
Confirm embedding and minimization; downstream relaxation may still be required
Multi-conformer docking
ETKDGv3 ensemble
Increase sampling until pose recovery or enrichment is stable
3D descriptors / pharmacophores
ETKDGv3 ensemble
Converge the reported statistic; justify energy/RMSD filters
Macrocycle / peptide
Macrocycle-aware ETKDG, then CREST if needed
Compare coverage against known conformers or downstream convergence
FEP input
Bound-pose-informed preparation and MD
Do not select solely by isolated-molecule conformer energy
Shape search
Query- and library-specific ensemble
Converge retrieval performance on a reference set
ETKDGv3 (Modern Default)
ETKDGv3 (Wang et al. 2020), building on the original ETKDG method (Riniker & Landrum 2015), incorporates experimental torsion preferences and updated macrocycle handling into distance geometry.
Goal: Generate an ensemble of 3D conformers from a SMILES with the modern default embedding algorithm.
Approach: Add explicit hydrogens, configure ETKDGv3 parameters (random seed, maximum embedding iterations, random coordinates), and embed multiple conformers via EmbedMultipleConfs.
useRandomCoords=True can improve convergence for macrocycles and highly flexible molecules. On an EmbedParameters object the documented limit is maxIterations; maxAttempts is only present in legacy positional overloads.
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.
defoptimize_conformers(mol, conf_ids, force_field='mmff94'):
results = []
if force_field == 'mmff94':
mmff_props = AllChem.MMFFGetMoleculeProperties(mol, mmffVariant='MMFF94s')
if mmff_props isnotNone:
for cid in conf_ids:
ff = AllChem.MMFFGetMoleculeForceField(mol, mmff_props, confId=cid)
if ff isNone:
results.append({'conf_id': cid, 'status': 'force_field_failed'})
continue
status = ff.Minimize(maxIts=1000)
results.append({'conf_id': cid, 'energy': ff.CalcEnergy(),
'converged': status == 0, 'force_field': 'MMFF94s'})
return results
force_field = 'uff'if force_field == 'uff':
ifnot AllChem.UFFHasAllMoleculeParams(mol):
raise ValueError('Neither MMFF94s nor UFF covers this molecule')
for cid in conf_ids:
ff = AllChem.UFFGetMoleculeForceField(mol, confId=cid)
if ff isNone:
results.append({'conf_id': cid, 'status': 'force_field_failed'})
continue
status = ff.Minimize(maxIts=1000)
results.append({'conf_id': cid, 'energy': ff.CalcEnergy(),
'converged': status == 0, 'force_field': 'UFF'})
return results
raise ValueError(f'Unsupported force field: {force_field}')
MMFF94 vs MMFF94s: MMFF94s is the static-structure variant, with modified out-of-plane and torsional terms intended to preserve planarity in selected functional groups. It is useful for geometry optimization, but it is not a universally preferred replacement for MMFF94; record which variant was used and validate it for the downstream task.
UFF (Universal Force Field): UFF has different and often broader parameter coverage than MMFF94, but RDKit does not guarantee coverage for every molecule and generic UFF does not validate metal coordination chemistry. Call UFFHasAllMoleculeParams before use and use a chemistry-appropriate method for metal complexes.
RMSD Pruning
Remove near-duplicate conformers within a chosen RMSD cutoff to keep the ensemble diverse:
import numpy as np
defprune_conformers_rmsd(mol, conf_ids, rmsd_cutoff=0.5):
n = len(conf_ids)
keep = []
for i, cid inenumerate(conf_ids):
is_unique = Truefor kept_cid in keep:
rmsd = AllChem.GetBestRMS(mol, mol, cid, kept_cid)
if rmsd < rmsd_cutoff:
is_unique = Falsebreakif is_unique:
keep.append(cid)
return keep
Typical RMSD cutoff (Source / Rationale):
Cutoff
Use case
Source
0.5 Å
Drug-like ensemble for descriptors / docking
Repository clustering heuristic; validate for the downstream task
1.0 Å
Drug-like ensemble for pharmacophore
Standard ROCS / pharmacophore practice
1.5-2.0 Å
Macrocycles / peptides
Repository clustering heuristic for higher conformational freedom
2.0+ Å
Cluster-centroid representative ensembles
Coarse representative sampling
Energy Window Filtering
Remove conformers above a project-justified energy cutoff only when the energy model and downstream purpose support that choice. Bound conformers can be strained relative to an isolated-molecule minimum.
deffilter_by_energy(mol, conf_ids, energies, window_kcal=10.0):
min_e = min(energies)
keep = []
for cid, e inzip(conf_ids, energies):
if e - min_e <= window_kcal:
keep.append(cid)
return keep
Treat any numerical energy window as a starting parameter. Calibrate it against conformer recovery or downstream metric convergence and record the energy method, solvent treatment, protonation state, and temperature assumptions.
Macrocycle Handling
Macrocycles (>=12 atom rings) have distinct conformational issues: ETKDGv3 default knowledge base under-samples macrocycle torsions. Use macrocycle-specific torsion preferences:
For difficult macrocycles, CREST + GFN2-xTB is a useful higher-cost option; validate coverage against experimental or downstream evidence rather than treating one method as universally definitive.
CREST + GFN2-xTB for High-Quality Sampling
CREST (Pracht et al. 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.
--gfn2: use GFN2-xTB; validate its coverage and energy ordering for the chemistry of interest.
--gfn-ff: use GFN-FF; it can reduce computational cost, but benchmark its coverage and energy ordering for the intended molecules.
-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
from pathlib import Path
defcrest_workflow(smiles, out_dir='crest_out'):
mol = Chem.MolFromSmiles(smiles)
if mol isNone:
raise ValueError(f'Invalid SMILES: {smiles!r}')
mol = Chem.AddHs(mol)
params = AllChem.ETKDGv3()
params.useRandomCoords = Trueif AllChem.EmbedMolecule(mol, params) == -1:
raise RuntimeError(f'Initial 3D embedding failed for {smiles!r}')
ifnot AllChem.MMFFHasAllMoleculeParams(mol):
raise ValueError(f'MMFF parameters are unavailable for {smiles!r}')
status = AllChem.MMFFOptimizeMolecule(
mol, mmffVariant='MMFF94s', maxIters=1000
)
if status != 0:
raise RuntimeError(f'Initial MMFF optimization did not converge (status {status})')
out_dir = Path(out_dir).resolve()
out_dir.mkdir(parents=True, exist_ok=True)
input_path = out_dir / 'input.xyz'
input_path.write_text(Chem.MolToXYZBlock(mol))
# Because cwd is out_dir, pass the coordinate filename rather than out_dir/input.xyz.
subprocess.run(['crest', input_path.name, '--gfn2', '-T', '12'],
cwd=out_dir, check=True)
output_path = out_dir / 'crest_conformers.xyz'ifnot output_path.exists():
raise FileNotFoundError(f'CREST did not produce {output_path}')
return output_path
Boltzmann Averaging of Properties
For ensemble descriptors (3D shape, dipole moment, polar surface area in 3D), Boltzmann-weight by energy:
import numpy as np
defboltzmann_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()
defboltzmann_average(values, energies, T=300.0):
w = boltzmann_weights(energies, T)
returnfloat(np.sum(np.array(values) * w))
Boltzmann populations require energies that approximate the relevant thermodynamic state, including conformer degeneracy and environmental effects where important. Raw gas-phase MMFF/UFF minima are exploratory surrogates, not generally validated populations.
ML-Based Conformer Generation and Search
Research methods such as GeoMol generate molecular conformations directly, while TorsionNet uses reinforcement learning to search torsional conformational space. They are distinct approaches and should not be presented as interchangeable drop-in generators.
# 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: Performance and coverage depend on the released model, training distribution, conformer definition, and benchmark. Verify the maintained implementation and benchmark it against ETKDGv3 or CREST on the intended chemistry before using it operationally; do not infer broad macrocycle or organometallic coverage from drug-like benchmarks.
Per-Tool Failure Modes
ETKDGv3 -- failed embedding
Trigger: Macrocycle, highly constrained polycyclic, or sterically crowded molecule.
Mechanism: Distance geometry cannot find a consistent 3D structure within the configured embedding iterations.
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: A fixed small ensemble can miss relevant minima for flexible molecules.
Symptom: New conformers continue to change cluster populations or the downstream result.
Fix: As a repository starting heuristic, use n_conf = max(10, 5 * NumRotatableBonds + 10), then increase sampling until the ensemble is stable for the downstream metric.
Single-conformer 3D descriptor
Trigger: Calculating 3D descriptors from a single conformer.
Mechanism: Some 3D descriptors vary materially across conformers.
Symptom: Same molecule produces different 3D descriptors on rerun.
Fix: Converge the descriptor over an ensemble and report the chosen summary. Use Boltzmann weighting only with a justified population model.
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 an exploratory lower-cost run or shorten metadynamics with the documented --mdlen/--len option. --noopt disables input pre-optimization; it does not skip metadynamics.
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
Use case
ETKDGv3
CREST
Drug-like organic molecule
Efficient starting point
Higher-cost comparison when convergence fails
Highly flexible molecule
Increase and convergence-test sampling
Useful alternative sampling strategy
Macrocycle or peptide
Try macrocycle-aware settings and validate
Often useful, but not automatically sufficient
Population-weighted descriptors
Requires justified energy/population model
Higher-level energy still requires thermodynamic validation
FEP input
Useful for initial coordinates
Does not replace bound-pose and MD preparation
For ETKDGv3 ensembles, compare a representative subset with an orthogonal method or experimental conformers and judge adequacy using the downstream metric; no single cross-method RMSD establishes completeness.
Common Errors
Symptom
Cause
Fix
EmbedMolecule returns -1
Embed failed
Set useRandomCoords=True; raise params.maxIterations
MMFFOptimize no-op
MMFF parameters missing
Use UFF fallback
All conformers identical
Stiff molecule
OK; molecule is rigid
Conformers physically wrong
Stereochemistry lost
Re-add explicit stereo before embedding
3D descriptors differ per run
Random seed not set
params.randomSeed = 42
CREST out-of-memory
Search/ensemble too large
Reduce --T, shorten documented sampling, or lower --ewin to retain fewer structures
Macrocycle ring inverted
Default torsion preferences wrong
Set useMacrocycleTorsions=True
AddHs not called
Implicit H not embedded
mol = Chem.AddHs(mol) before EmbedMolecule
References
Hawkins et al., J. Chem. Inf. Model. 50:572-584 (2010) -- OMEGA conformer sampling (DOI 10.1021/ci100031x).
Riniker & Landrum, J. Chem. Inf. Model. 55:2562-2574 (2015) -- original ETKDG method (DOI 10.1021/acs.jcim.5b00654).
Wang S, Witek J, Landrum GA, Riniker S. J. Chem. Inf. Model. 60:2044-2058 (2020) -- ETKDGv3 macrocycle update (DOI 10.1021/acs.jcim.0c00025).
Halgren TA, J. Comput. Chem. 17:490-519 (1996) -- MMFF94 force field (DOI 10.1002/(SICI)1096-987X(199604)17:5/6%3C490::AID-JCC1%3E3.0.CO;2-P).
Rappe AK et al., J. Am. Chem. Soc. 114:10024-10035 (1992) -- UFF (DOI 10.1021/ja00051a040).
Pracht P et al., J. Chem. Phys. 160:114110 (2024) -- CREST 3.0 (DOI 10.1063/5.0197592).
Bannwarth C, Ehlert S, Grimme S. J. Chem. Theory Comput. 15:1652-1671 (2019) -- GFN2-xTB (DOI 10.1021/acs.jctc.8b01176).
