bio-conformer-generation
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill conformer-generation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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-validatio
- Version Compatibility
- Conformer Method Taxonomy
- Decision Tree by Scenario
- ETKDGv3 (Modern Default)
- Force-Field Optimization
- RMSD Pruning
- Energy Window Filtering
- Macrocycle Handling
- CREST + GFN2-xTB for High-Quality Sampling
- Boltzmann Averaging of Properties
- ML-Based Conformer Generation (GeoMol, TorsionNet)
- Per-Tool Failure Modes
- ETKDGv3 -- failed embedding
- MMFF94 -- parameter missing
xtb mol.xyz --opt extreme crest opt.xyz --gfn2 --T 12 -ewin 6
What does the bio-conformer-generation skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill conformer-generation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.