drug-pocket-detection
drug-pocket-detection is a Bioinformatics & life sciences skill for AI agents, published by learningmatter-mit in the AtomisticSkills repository on GitHub. Skillspace rates drug-pocket-detection D (69/100) and it passed the safety scan; the rating is based on static analysis only.
Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank). Returns ranked pockets with lining residues, geometric center, volume, and a druggability score per pocket. Excludes docking; pair with drug-binding-site-definition or drug-docking-vina downstream. Use whenever the user has a protein but no binding-site information, asks about cryptic / allosteric / orphan pockets, needs to assess druggability, or wants to choose where to dock.
- Grade
- D
- Quality
- 69 / 100
- Safety
- Passed
- GitHub stars
- 132
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 78 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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