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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PharMolix/OpenBioMed --skill drug-lead-analysisgit clone --depth 1 https://github.com/PharMolix/OpenBioMedWrote 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-lead-analysis)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/drug-lead-analysis"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/drug-lead-analysis/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-lead-analysis"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/drug-lead-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00091 | $0.01335 |
| Opus 5 | $0.00046 | $0.00668 |
| Sonnet 5 | $0.00018 | $0.00267 |
| Haiku 4.5 | $0.00009 | $0.00134 |
Grade A, and why
drug-lead-analysis scanned grade A with 0 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 12d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drug Lead Analysis
This skill guides you through a comprehensive analysis of drug candidate molecules using OpenBioMed's molecular analysis tools.
When to Use This Skill
- User asks to analyze a molecule for drug potential
- User provides a molecule name or SMILES and wants an evaluation
- User asks about drug-likeness, ADMET, BBB penetration, or side effects
- User wants to compare multiple molecules for lead optimization
Analysis Workflow
Step 1: Get the Molecule
First, obtain the molecule object:
If user provides a molecule name (e.g., "aspirin", "ibuprofen"):
from open_biomed.tools import TOOLS
tool = TOOLS["molecule_name_request"]
result, message = tool.run(name="aspirin")
molecule = result["molecule"]
print(message) # Shows retrieved info
If user provides a SMILES string:
from open_biomed.data import Molecule
molecule = Molecule.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")
If user provides a SDF file:
molecule = Molecule.from_sdf_file("path/to/molecule.sdf")
Step 2: Calculate Drug-likeness Scores
Run all drug-likeness metrics:
from open_biomed.tools import TOOLS
# QED (Quantitative Estimate of Drug-likeness) - 0 to 1, higher is better
qed_tool = TOOLS["molecule_qed"]
qed_result, qed_msg = qed_tool.run(molecule=molecule)
# SA (Synthetic Accessibility) - 1 to 10, lower is easier to synthesize
sa_tool = TOOLS["molecule_sa"]
sa_result, sa_msg = sa_tool.run(molecule=molecule)
# LogP (lipophilicity) - ideally between -0.4 and 5.6
logp_tool = TOOLS["molecule_logp"]
logp_result, logp_msg = logp_tool.run(molecule=molecule)
# Lipinski's Rule of Five - count violations (0 is ideal)
lipinski_tool = TOOLS["molecule_lipinski"]
lipinski_result, lipinski_msg = lipinski_tool.run(molecule=molecule)
Step 3: Predict ADMET Properties
Use the property prediction models:
# Blood-brain barrier penetration (binary: penetrates or not)
prop_tool = TOOLS["molecule_property_prediction"]
bbb_result, bbb_msg = prop_tool.run(
molecule=molecule,
dataset="bbbp",
model="graphmvp"
)
# Side effects prediction (27 categories from SIDER dataset)
sidefx_result, sidefx_msg = prop_tool.run(
molecule=molecule,
dataset="sider",
model="graphmvp"
)
What ships with it
2 files 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.
- 12d ago First seen · 176 lines · 91 tokens per session scan A 12977ff22d77
drug-lead-analysis is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 1,335 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
molecular-optimization
Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).
admet-prediction
ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) prediction for drug candidates. Use for assessing drug-likeness, PK properties, and safety risks early in drug discovery. Keywords: ADMET, PK, toxicity, drug-likeness, DILI, hERG, bioavailability.
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…
admet-reasoning
Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
cheminformatics
Molecular structure analysis, SAR triage, compound library characterization, QSAR modeling, ADMET prediction, chemical space visualization, target engagement assessment, drug perturbation connectivity scoring, and selectivity profiling.
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…