Diffusion-based molecular docking. Predict protein-ligand... (Docker + Debugging)
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.
Core Features
Ready to Use
Quick integration into your workflow with minimal setup
Community Verified
Active open-source community with continuous updates
Completely Free
MIT/Apache licensed for commercial and personal use
Flexible Extension
Customizable and extendable based on your needs
How to Use
2Install to Claude
Place the skill file in Claude's skills directory (usually ~/.claude/skills/）。
3Start Using
Restart Claude or run the reload command to load the skill
Tip: Read the documentation and code carefully before first use to understand functionality and permission requirements
Related Tags
Technical Information
All Skills from open-source community, preserving original authors' copyrights
K-Dense-AI__claude-scientific-skills/scientific-skills/diffdock/Skill.mdRelated Skills
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This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Claude Code agent generation system that creates custom agents and sub-agents with enhanced YAML frontmatter, tool access patterns, and MCP integration support following proven production patterns