distance-geometry-embedding
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Generate initial 3D molecular coordinates from 2D SMILES or MOL structures using RDKit's distance-geometry algorithm, producing a diverse ensemble of conformers that serve as starting geometries for subsequent force-field optimization and quantum filtering in CCS prediction workflows.
When to use
When you have ionized adduct structures (in SMILES or MOL format) from an upstream ionization-state determination step and need to produce multiple low-energy 3D conformations for collision cross section prediction, metabolite annotation, or structure-property modeling. Use this skill when conformational diversity is required to adequately sample the potential energy surface before quantum refinement.
When NOT to use
- Input molecules are already 3D structures with pre-optimized geometries suitable for immediate quantum calculation; distance-geometry embedding adds unnecessary computational overhead.
- Your workflow requires conformers at a specific energy level (e.g., QM-optimized); raw distance-geometry + MM minimization produces structures far from the QM ground state and will require subsequent re-optimization.
- Molecules contain non-standard elements or functional groups outside RDKit's MMFF94/UFF parametrization, causing minimization failures or invalid geometries.
Inputs
- Ionized molecular structures (SMILES strings or MOL files)
- Integer parameter for number of conformers to generate per molecule (e.g., 50–500)
Outputs
- 3D conformer ensemble (SDF or pickle format)
- Multiple conformations per molecule with hydrogens added and force-field-minimized geometries
How to apply
Load ionized adduct structures output by Dimorphite-DL or similar ionization tools. Use RDKit's AllChem.EmbedMolecule function to generate multiple 3D conformers per molecule (typically 50–500 samples) via distance-geometry embedding, which builds 3D coordinates from inter-atomic distance constraints derived from the 2D connectivity graph. Add hydrogens to each structure before embedding. After embedding, minimize each conformer's geometry using RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima. Export the complete conformer ensemble to SDF or pickle format, retaining all conformers for downstream filtering by ASE-ANI or other methods. The rationale is that distance-geometry produces diverse initial structures with reasonable bond and angle geometry without quantum cost, enabling efficient sampling of conformational space before expensive quantum calculations.
Related tools
- RDKit (Performs distance-geometry embedding via AllChem.EmbedMolecule, adds hydrogens, and applies force-field geometry minimization (MMFF94/UFF) to each conformer) — https://www.rdkit.org
- Dimorphite-DL (Upstream tool that produces ionized adduct structures (SMILES/MOL format) that serve as input to distance-geometry embedding) — https://durrantlab.pitt.edu/dimorphite-dl
- ASE-ANI (Downstream conformation filtering tool that uses neural-network potentials to rank and filter conformers generated by distance-geometry embedding before quantum calculations) — https://github.com/isayev/ASE_ANI
- Snakemake (Workflow orchestration tool that automates and parallelizes distance-geometry embedding across multiple molecules on HPC systems) — https://github.com/DasSusanta/snakemake_ccs
Examples
from rdkit import Chem; from rdkit.Chem import AllChem; mol = Chem.MolFromSmiles('[NH3+]C(C)C'); Chem.AddHs(mol); AllChem.EmbedMolecule(mol, numConfs=100); AllChem.MMFFOptimizeMoleculeConfs(mol); Chem.SDWriter('conformers.sdf').write(mol)
Evaluation signals
- Conformer ensemble contains the expected number of 3D structures per input molecule (e.g., 50–500 conformers per molecule).
- All conformers are valid chemical structures with correct bond orders, valences, and hydrogen counts; no NaN or invalid coordinates in SDF/pickle output.
- Geometric validity: inter-atomic distances respect van der Waals constraints and typical bond/angle ranges (e.g., C–C bond ≈ 1.5 Å, C–H ≈ 1.1 Å).
- Force-field minimization convergence: reported energy per conformer decreases from initial embedding to final minimized state, indicating successful local geometry relaxation.
- Diversity of conformers: RMSD between conformers is > 0.5–1.0 Å (relative to heavy atoms), confirming that distance-geometry sampling explores distinct conformational regions rather than collapsing to a single structure.
Limitations
- Distance-geometry embedding relies on RDKit's distance-bound matrix, which can fail for highly constrained or exotic ring systems, producing invalid or distorted 3D geometries.
- MMFF94 and UFF force fields are not parametrized for all elements; molecules containing transition metals, halogens beyond Cl, or other heavy atoms may fail minimization or produce unreliable geometries.
- Force-field minimization reaches only local minima; resulting conformers are not QM-optimized and may be far from ground state. Subsequent quantum filtering (ASE-ANI, QUICK) is essential to identify lower-energy structures.
- ASE-ANI repository is deprecated and no longer supported; users are directed to use TorchANI implementation instead, which may have different input/output formats.
- Conformer quality depends critically on the number of embedding attempts and force-field choice; empirically, 50–500 conformers may be insufficient for large, flexible molecules or those with multiple stable conformational isomers.
Evidence
- [other] Use RDKit to add hydrogens and generate initial 3D coordinates via distance-geometry algorithm.: "Use RDKit to add hydrogens and generate initial 3D coordinates via distance-geometry algorithm"
- [other] Generate multiple conformers per structure using RDKit's AllChem.EmbedMolecule with specified number of conformers (e.g., 50–500 samples per molecule).: "Generate multiple conformers per structure using RDKit's AllChem.EmbedMolecule with specified number of conformers (e.g., 50–500 samples per molecule)"
- [other] Minimize each conformer's geometry using RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima.: "Minimize each conformer's geometry using RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima"
- [other] RDKit is used in the workflow as the conformation generation tool that operates on ionized adduct structures produced by Dimorphite-DL: "RDKit is used in the workflow as the conformation generation tool that operates on ionized adduct structures produced by Dimorphite-DL"
- [readme] This is a prototype interface for ANI-1x and ANI-1ccx neural network potentials for The Atomic Simulation Environment (ASE).: "This is a prototype interface for ANI-1x and ANI-1ccx neural network potentials for The Atomic Simulation Environment (ASE)"
1---2name: distance-geometry-embedding3description: Use when when you have ionized adduct structures (in SMILES or MOL format) from an upstream ionization-state determination step and need to produce multiple low-energy 3D conformations for collision cross section prediction, metabolite annotation, or structure-property modeling.4license: CC-BY-4.05---67# distance-geometry-embedding89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112Generate initial 3D molecular coordinates from 2D SMILES or MOL structures using RDKit's distance-geometry algorithm, producing a diverse ensemble of conformers that serve as starting geometries for subsequent force-field optimization and quantum filtering in CCS prediction workflows.1314## When to use1516When you have ionized adduct structures (in SMILES or MOL format) from an upstream ionization-state determination step and need to produce multiple low-energy 3D conformations for collision cross section prediction, metabolite annotation, or structure-property modeling. Use this skill when conformational diversity is required to adequately sample the potential energy surface before quantum refinement.1718## When NOT to use1920- Input molecules are already 3D structures with pre-optimized geometries suitable for immediate quantum calculation; distance-geometry embedding adds unnecessary computational overhead.21- Your workflow requires conformers at a specific energy level (e.g., QM-optimized); raw distance-geometry + MM minimization produces structures far from the QM ground state and will require subsequent re-optimization.22- Molecules contain non-standard elements or functional groups outside RDKit's MMFF94/UFF parametrization, causing minimization failures or invalid geometries.2324## Inputs2526- Ionized molecular structures (SMILES strings or MOL files)27- Integer parameter for number of conformers to generate per molecule (e.g., 50–500)2829## Outputs3031- 3D conformer ensemble (SDF or pickle format)32- Multiple conformations per molecule with hydrogens added and force-field-minimized geometries3334## How to apply3536Load ionized adduct structures output by Dimorphite-DL or similar ionization tools. Use RDKit's AllChem.EmbedMolecule function to generate multiple 3D conformers per molecule (typically 50–500 samples) via distance-geometry embedding, which builds 3D coordinates from inter-atomic distance constraints derived from the 2D connectivity graph. Add hydrogens to each structure before embedding. After embedding, minimize each conformer's geometry using RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima. Export the complete conformer ensemble to SDF or pickle format, retaining all conformers for downstream filtering by ASE-ANI or other methods. The rationale is that distance-geometry produces diverse initial structures with reasonable bond and angle geometry without quantum cost, enabling efficient sampling of conformational space before expensive quantum calculations.3738## Related tools3940- **RDKit** (Performs distance-geometry embedding via AllChem.EmbedMolecule, adds hydrogens, and applies force-field geometry minimization (MMFF94/UFF) to each conformer) — https://www.rdkit.org41- **Dimorphite-DL** (Upstream tool that produces ionized adduct structures (SMILES/MOL format) that serve as input to distance-geometry embedding) — https://durrantlab.pitt.edu/dimorphite-dl42- **ASE-ANI** (Downstream conformation filtering tool that uses neural-network potentials to rank and filter conformers generated by distance-geometry embedding before quantum calculations) — https://github.com/isayev/ASE_ANI43- **Snakemake** (Workflow orchestration tool that automates and parallelizes distance-geometry embedding across multiple molecules on HPC systems) — https://github.com/DasSusanta/snakemake_ccs4445## Examples4647```48from rdkit import Chem; from rdkit.Chem import AllChem; mol = Chem.MolFromSmiles('[NH3+]C(C)C'); Chem.AddHs(mol); AllChem.EmbedMolecule(mol, numConfs=100); AllChem.MMFFOptimizeMoleculeConfs(mol); Chem.SDWriter('conformers.sdf').write(mol)49```5051## Evaluation signals5253- Conformer ensemble contains the expected number of 3D structures per input molecule (e.g., 50–500 conformers per molecule).54- All conformers are valid chemical structures with correct bond orders, valences, and hydrogen counts; no NaN or invalid coordinates in SDF/pickle output.55- Geometric validity: inter-atomic distances respect van der Waals constraints and typical bond/angle ranges (e.g., C–C bond ≈ 1.5 Å, C–H ≈ 1.1 Å).56- Force-field minimization convergence: reported energy per conformer decreases from initial embedding to final minimized state, indicating successful local geometry relaxation.57- Diversity of conformers: RMSD between conformers is > 0.5–1.0 Å (relative to heavy atoms), confirming that distance-geometry sampling explores distinct conformational regions rather than collapsing to a single structure.5859## Limitations6061- Distance-geometry embedding relies on RDKit's distance-bound matrix, which can fail for highly constrained or exotic ring systems, producing invalid or distorted 3D geometries.62- MMFF94 and UFF force fields are not parametrized for all elements; molecules containing transition metals, halogens beyond Cl, or other heavy atoms may fail minimization or produce unreliable geometries.63- Force-field minimization reaches only local minima; resulting conformers are not QM-optimized and may be far from ground state. Subsequent quantum filtering (ASE-ANI, QUICK) is essential to identify lower-energy structures.64- ASE-ANI repository is deprecated and no longer supported; users are directed to use TorchANI implementation instead, which may have different input/output formats.65- Conformer quality depends critically on the number of embedding attempts and force-field choice; empirically, 50–500 conformers may be insufficient for large, flexible molecules or those with multiple stable conformational isomers.6667## Evidence6869- [other] Use RDKit to add hydrogens and generate initial 3D coordinates via distance-geometry algorithm.: "Use RDKit to add hydrogens and generate initial 3D coordinates via distance-geometry algorithm"70- [other] Generate multiple conformers per structure using RDKit's AllChem.EmbedMolecule with specified number of conformers (e.g., 50–500 samples per molecule).: "Generate multiple conformers per structure using RDKit's AllChem.EmbedMolecule with specified number of conformers (e.g., 50–500 samples per molecule)"71- [other] Minimize each conformer's geometry using RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima.: "Minimize each conformer's geometry using RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima"72- [other] RDKit is used in the workflow as the conformation generation tool that operates on ionized adduct structures produced by Dimorphite-DL: "RDKit is used in the workflow as the conformation generation tool that operates on ionized adduct structures produced by Dimorphite-DL"73- [readme] This is a prototype interface for ANI-1x and ANI-1ccx neural network potentials for The Atomic Simulation Environment (ASE).: "This is a prototype interface for ANI-1x and ANI-1ccx neural network potentials for The Atomic Simulation Environment (ASE)"
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