drug-candidate-discovery
drug-candidate-discovery is a Bioinformatics & life sciences skill for AI agents, published by PharMolix in the OpenBioMed repository on GitHub. Skillspace rates drug-candidate-discovery F (52/100) and it passed the safety scan; the rating is based on static analysis only.
Generate diverse druggable molecules for a given target or disease using OpenBioMed's AI-powered drug discovery tools. Use this skill when: (1) Generating drug candidates, molecules, or compounds for a target/disease, (2) Performing structure-based drug design or de novo drug design, (3) Finding or creating molecules that bind to a specific protein target, (4) Discovering potential drugs for a disease name, (5) Designing molecules with specific properties (LogP, QED, docking scores). The skill handles target identification, structure retrieval, molecule generation, and in silico evaluation.
- Grade
- F
- Quality
- 52 / 100
- Safety
- Passed
- GitHub stars
- 1,090
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 59 / 100
- Maturity
- Basic: a single SKILL.md
- 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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