molecular-dynamics
Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
Content Preview
---
name: molecular-dynamics
description: Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein/small molecule systems, define force fields, run energy minimization and production MD, analyze trajectories (RMSD, RMSF, contact maps, free energy surfaces). For structural biology, drug binding, and biophysics.
license: MIT
metadata:
skill-author: Kuan-lin Huang
---
# Molecular Dynamics
## Overview
Molecular dynamics (MD) simulation computationally models tHow to Use
Recommended: Install to project (local)
mkdir -p .claude/skills
curl -o .claude/skills/molecular-dynamics.md \
https://raw.githubusercontent.com/K-Dense-AI/claude-scientific-skills/main/scientific-skills/molecular-dynamics/SKILL.mdSkill is scoped to this project only. Add .claude/skills/ to your .gitignoreif you don't want to commit it.
Alternative: Clone full repo
git clone https://github.com/K-Dense-AI/claude-scientific-skillsThen reference at scientific-skills/molecular-dynamics/SKILL.md
Related Skills
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
biopythonbiopython
by K-Dense-AI · claude-scientific-skills
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
deepchemdeepchemml
by K-Dense-AI · claude-scientific-skills
diffdock
Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
diffdockdiffdock
by K-Dense-AI · claude-scientific-skills
molfeat
Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular ML.
molfeatmolfeatml
by K-Dense-AI · claude-scientific-skills