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 admet-predictiongit 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/admet-prediction)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/admet-prediction"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/admet-prediction/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/admet-prediction"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/admet-prediction.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 | $0.00096 | $0.01238 |
| Opus 5 | $0.00048 | $0.00619 |
| Sonnet 5 | $0.00019 | $0.00248 |
| Haiku 4.5 | $0.00010 | $0.00124 |
Grade A, and why
admet-prediction 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 11d 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ADMET Prediction
Predict comprehensive ADMET properties for drug candidate molecules using GraphMVP ensemble models.
When to Use
- User asks to predict ADMET properties for a molecule
- User provides a drug candidate and wants safety assessment
- User needs blood-brain barrier penetration prediction
- User wants to evaluate toxicity (LD50) or side effects (SIDER)
- User requests pharmacokinetic properties (half-life, Caco-2)
Workflow
Step 1: Load Molecule
Create molecule from SMILES string.
from open_biomed.data import Molecule
molecule = Molecule.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O") # Aspirin
Step 2: Build ADMET Pipeline
Initialize ensemble pipeline with all GraphMVP checkpoints.
from open_biomed.core.pipeline import InferencePipeline, EnsemblePipeline
pipelines = {
"BBBP": InferencePipeline(
task="molecule_property_prediction", model="graphmvp",
model_ckpt="./checkpoints/server/graphmvp-BBBP.ckpt",
additional_config="./configs/dataset/bbbp.yaml", device="cuda:0"),
"SIDER": InferencePipeline(
task="molecule_property_prediction", model="graphmvp",
model_ckpt="./checkpoints/server/graphmvp-SIDER.ckpt",
additional_config="./configs/dataset/sider.yaml", device="cuda:0"),
# See examples/basic_example.py for full pipeline setup
}
pipeline = EnsemblePipeline(pipelines)
Step 3: Run Predictions
Execute predictions for each ADMET property.
# BBB penetration
bbb_result = pipeline.run(molecule=molecule, task="BBBP")
# Side effects (27 categories)
sider_result = pipeline.run(molecule=molecule, task="SIDER")
# Regression properties
caco2_result = pipeline.run(molecule=molecule, task="caco2_wang")
half_life_result = pipeline.run(molecule=molecule, task="half_life_obach")
ld50_result = pipeline.run(molecule=molecule, task="ld50_zhu")
Expected Outputs
| Task | Output Type | Description |
|---|---|---|
| BBBP | float [0-1] | Probability of BBB penetration |
| SIDER | list[27 floats] | Side effect probabilities per category |
| caco2_wang | float | Log permeability (cm/s) |
| half_life_obach | float | Log half-life (hours) |
| ld50_zhu | float | Log LD50 (mg/kg) |
What ships with it
3 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.
- 11d ago First seen · 166 lines · 96 tokens per session scan A 0e95ab6f7480
admet-prediction is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 1,238 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
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).
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…
molecular-optimization
Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).
target-safety
Target safety assessment including known toxicities, essentiality, and off-target concerns. Use for early safety risk evaluation and target selection. Keywords: target safety, toxicity, essential gene, knockout, safety risks.
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…