OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pharmolix/openbiomed/drug-candidate-discovery)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/drug-candidate-discovery"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/drug-candidate-discovery/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pharmolix/openbiomed/drug-candidate-discovery"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/drug-candidate-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00128 | $0.07775 |
| Opus 5 | $0.00064 | $0.03887 |
| Sonnet 5 | $0.00026 | $0.01555 |
| Haiku 4.5 | $0.00013 | $0.00777 |
Grade B, and why
drug-candidate-discovery scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post( "https://search.rcsb.org/rcsbsearch/v2/query", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.post( How it starts
The opening of the file, as written. The whole thing — 869 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drug Candidate Discovery
This skill uses the OpenBioMed repository to generate diverse druggable molecules for a given target or disease. It orchestrates a complete drug discovery workflow from target identification to candidate evaluation.
CRITICAL REQUIREMENTS
You MUST execute code to produce actual outputs. Writing scripts without running them is NOT acceptable.
Required outputs that MUST be created:
- SDF files containing 3D molecular structures
- Visualization files (PNG images of molecules and complexes)
- Comprehensive markdown report
After writing any script, you MUST run it using the Bash tool to generate the actual outputs.
Inputs
The user should provide:
- target_or_disease (required): Name of the target protein or disease (e.g., "BTK", "Alzheimer's disease", "KRAS G12C")
- num_candidates (optional, default=5): Number of desired candidate molecules
- property_constraints (optional): Desired molecular properties as a dictionary:
logp_min,logp_max: LogP range (e.g., -1 to 3)qed_min: Minimum QED score (e.g., 0.5)vina_max: Maximum Vina docking score in kcal/mol (e.g., -8)sa_min: Minimum synthetic accessibility score (e.g., 0.5)
- device (optional, default="cuda:0"): GPU device for running models
- model_ckpt (optional, default="./checkpoints/molcraft/last_updated.ckpt"): Path to MolCraft checkpoint
- max_attempts (optional, default=100): Maximum generation/optimization cycles
Workflow Overview
Phase 1: Target Identification & Research
CRITICAL: You MUST search web databases to find PDB structures with bound ligands. Do NOT use hardcoded PDB IDs.
-
Web Search for Target Information
- Use
WebSearchtool to search for: "{target_name} protein drug target UniProt PDB structure" - Identify the UniProt ID for the target protein
- Find known inhibitors/drugs and their PDB co-crystal structures
- Use
-
Query UniProt for Protein Metadata
- Use
UniProtRequestertool with the UniProt ID - Extract: protein name, gene name, organism, disease associations
- Use
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 869 lines · 128 tokens per session scan B a272883c8e2d
drug-candidate-discovery is a skill published in the GitHub repository PharMolix/OpenBioMed (1,105 stars, last pushed 1mo ago), licensed MIT. It adds 128 tokens to every session and 7,775 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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omics-tools
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grn-tools
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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…