bio-conformer-generation

name	bio-conformer-generation
description	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.
origin	openai4s
category	bioskills/chemoinformatics
metadata	{"tool_type":"mixed","primary_tool":"RDKit","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

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 (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.

Conformer Method Taxonomy

Method	Cost / mol	Quality	Use case	Fails when
ETKDGv3 + MMFF94	Benchmark on actual molecules/hardware	Useful for many drug-like organics	Initial docking/descriptors	Difficult macrocycles, peptides, unsupported chemistry
ETKDGv3 + UFF	Fast	Different parameter coverage from MMFF94	Fallback only after checking UFF parameters	Unsupported atom types; coordination chemistry
Omega (OpenEye)	Benchmark licensed workflow	Commercial conformer generator	Commercial pipelines	License cost and configured limits
Confab (Open Babel)	Benchmark on intended chemistry	Systematic torsion search	Alternative enumeration	Combinatorial growth and force-field dependence
RDKit ETKDGv3 + macrocycle preferences	Molecule-dependent	Macrocycle-aware embedding	Macrocyclic starting ensembles	Coverage remains molecule-dependent
CREST + GFN2-xTB	Molecule/settings-dependent	Semiempirical conformational sampling	Difficult flexible molecules	Computational cost; special chemistry
CREST + GFN-FF	Lower cost than GFN2-xTB	Force-field-level sampling	Exploratory sampling	Validate coverage and ordering for the chemistry
GeoMol (Ganea 2021)	Hardware/model-dependent	Learned conformer generation	Large-library research workflow	Training distribution and released-model coverage
TorsionNet (Gogineni 2020)	Hardware/model-dependent	Learned torsional search	Research workflow	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.

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.maxIterations = 1000
    ids = AllChem.EmbedMultipleConfs(mol, numConfs=n_conf, params=params)
    return mol, list(ids)

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.

def optimize_conformers(mol, conf_ids, force_field='mmff94'):
    results = []
    if force_field == 'mmff94':
        mmff_props = AllChem.MMFFGetMoleculeProperties(mol, mmffVariant='MMFF94s')
        if mmff_props is not None:
            for cid in conf_ids:
                ff = AllChem.MMFFGetMoleculeForceField(mol, mmff_props, confId=cid)
                if ff is None:
                    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':
        if not 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 is None:
                results.append({'conf_id': cid, 'status': 'force_field_failed'})
                
            status = ff.Minimize(maxIts=)
            results.append({: cid, : ff.CalcEnergy(),
                            : status == , : })
         results
     ValueError()

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

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):

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.

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

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:

from rdkit.Chem import AllChem

def macrocycle_conformers(smiles, n_conf=200, seed=42):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        raise ValueError(f'Invalid SMILES: {smiles!r}')
    mol = Chem.AddHs(mol)
    params = AllChem.ETKDGv3()
    params.randomSeed = seed
    params.useRandomCoords = True
    params.useMacrocycleTorsions = True
    params.useSmallRingTorsions = True
    params.maxIterations = 5000
    ids = list(AllChem.EmbedMultipleConfs(mol, numConfs=n_conf, params=params))
    if not ids:
        raise RuntimeError(f'No macrocycle conformers embedded for {smiles!r}')
    return mol, ids

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.

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

--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

def crest_workflow(smiles, out_dir='crest_out'):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        raise ValueError(f'Invalid SMILES: {smiles!r}')
    mol = Chem.AddHs(mol)
    params = AllChem.ETKDGv3()
    params.useRandomCoords = True
    if AllChem.EmbedMolecule(mol, params) == -1:
        raise RuntimeError(f'Initial 3D embedding failed for {smiles!r}')
    if not 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=)
    output_path = out_dir / 
      output_path.exists():
         FileNotFoundError()
     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

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))

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.

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

Fix: Set useRandomCoords=True, increase maxIterations; 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: 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).
• RDKit force-field API: https://www.rdkit.org/docs/source/rdkit.Chem.rdForceFieldHelpers.html
• CREST command-line documentation: https://crest-lab.github.io/crest-docs/page/documentation/keywords.html
• xTB documentation: https://xtb-docs.readthedocs.io/
• Ganea et al., NeurIPS (2021) -- GeoMol ML conformer generation.
• Gogineni T et al., NeurIPS 33 (2020) -- TorsionNet learned conformational search.

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