Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Quick integration into your workflow with minimal setup
Active open-source community with continuous updates
MIT/Apache licensed for commercial and personal use
Customizable and extendable based on your needs
Download or copy the skill file from the source repository
Place the skill file in Claude's skills directory (usually ~/.claude/skills/)。
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
All Skills from open-source community, preserving original authors' copyrights
K-Dense-AI__claude-scientific-skills/scientific-skills/torchdrug/Skill.mdProven benefits and measurable impact
Reduce time spent identifying YAML syntax issues significantly
Cut deployment errors caused by invalid YAML configs in half
Complete confidence your YAML files will always load
Perfect for these scenarios
Quickly identify and fix syntax errors in broken YAML files
Ensure pipeline configurations parse correctly before deployment
Verify how your tools handle malformed YAML edge cases
Prevent issues from unparsable configs breaking your systems