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chem-conformer-search

Generate and rank relaxed molecular conformer ensembles with Boltzmann weighting.

144|21|Updated Jan 8, 2026
Search Tags:#molecular simulation#conformer generation#rdkit etkdg#mlip relaxation#boltzmann weighting#molecular energy ranking
One-click install
npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill chem-conformer-search
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chem-conformer-search
Source: https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-conformer-search
Command: npx skills add https://github.com/learningmatter-mit/AtomisticSkills --skill chem-conformer-search

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, sklearn, numpy, ase, torch, rdkit, scipy.cluster, scipy.spatial, sklearn.cluster, and includes scripts (resource) components.

What problem does it solve?

It finds a diverse set of low-energy 3D molecular conformations for a given molecule, so you can prioritize the most physically relevant structures for downstream modeling or calculations.

Core Features & Use Cases

  • Conformer generation (RDKit ETKDG): Creates an initial ensemble from a SMILES string or an input structure file.
  • MLIP relaxation: Optimizes each conformer with a selectable ML interatomic potential backend (e.g., MACE, MatGL, FairChem) to obtain near-DFT-quality geometries and energies.
  • Deduplication and energy ranking with Boltzmann weighting: Removes redundant conformers via RMSD thresholding or clustering (hierarchical/k-means), then ranks and assigns finite-temperature populations.
  • Use Case: Given an ibuprofen-like drug with many rotatable bonds, generate a conformer ensemble and extract a compact, low-energy, temperature-relevant set for tasks like docking preparation or conformational preference analysis.

Quick Start

Use the chem-conformer-search skill to generate and MLIP-relax conformers from your SMILES input and write the relaxed ranked ensemble and Boltzmann-weighted results to an output directory.

Frequently Asked Questions about chem-conformer-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate low-energy molecular conformers from a SMILES string?▼

To generate low-energy molecular conformers from a SMILES string, use this skill to create an initial ensemble with RDKit ETKDG and optimize geometries using MLIP relaxation for near-DFT quality energies.

What is Boltzmann weighting for conformer energy ranking?▼

Boltzmann weighting for conformer energy ranking assigns finite-temperature populations to deduplicated molecular conformers, helping prioritize physically relevant 3D structures for conformational preference analysis.

Can I use RDKit with machine learning interatomic potentials for molecular simulation?▼

Yes, you can use RDKit with machine learning interatomic potentials for molecular simulation by generating initial conformers with RDKit ETKDG and relaxing them using selectable MLIP backends like MACE, MatGL, or FairChem.

What's the best way to deduplicate molecular conformer ensembles?▼

The best way to deduplicate molecular conformer ensembles is applying RMSD thresholding or clustering techniques like hierarchical or k-means clustering to remove redundant 3D molecular structures before energy ranking.

Does conformer generation output XYZ files for downstream docking preparation?▼

Conformer generation outputs JSON and XYZ files containing relative energies and Boltzmann weights, providing relaxed ranked ensembles needed for downstream workflows like docking preparation or candidate selection.

Do I need scipy and sklearn to run molecular energy ranking with clustering?▼

Yes, you need scipy and sklearn to run molecular energy ranking with clustering, as these dependencies provide the hierarchical and k-means clustering algorithms required for removing redundant conformer structures.

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