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.
Borrowing it
Nothing to install: this file belongs to PharMolix/OpenBioMed. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/PharMolix/OpenBioMed/main/CLAUDE.mdgit 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/instructions/pharmolix/openbiomed/claude-md)<a href="https://agentmods.dev/instructions/pharmolix/openbiomed/claude-md"><img src="https://agentmods.dev/badge/instructions/pharmolix/openbiomed/claude-md/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/instructions/pharmolix/openbiomed/claude-md"><img src="https://agentmods.dev/badge/instructions/pharmolix/openbiomed/claude-md.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 | $0.01523 | $0.01523 |
| Opus 5 | $0.00762 | $0.00762 |
| Sonnet 5 | $0.00305 | $0.00305 |
| Haiku 4.5 | $0.00152 | $0.00152 |
Grade A, and why
OpenBioMed CLAUDE.md 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 9d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Project Overview
OpenBioMed is a Python deep learning toolkit for AI-empowered biomedicine. It provides flexible APIs for multi-modal biomedical data (molecules, proteins, pockets, cells, text) and includes 20+ tools for downstream applications including drug discovery, protein engineering, and multi-modal reasoning.
Key Commands
Environment Setup
conda create -n OpenBioMed python=3.9
conda activate OpenBioMed
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.1+cu117.html
pip install pytorch_lightning==2.0.8 peft==0.9.0 accelerate==1.3.0 --no-deps
pip install -r requirements.txt
pip install -e .
Training
# Using shell script
./scripts/train.sh TASK MODEL DATASET GPU_ID
# Using Python directly
python open_biomed/scripts/train.py \
--task TASK \
--additional_config_file configs/model/MODEL.yaml \
--dataset_name DATASET \
--dataset_path ./datasets/TASK/DATASET
Testing/Evaluation
./scripts/test.sh TASK MODEL DATASET GPU_ID
Inference
python open_biomed/scripts/inference.py --task TASK_NAME
Running Server
python -m uvicorn open_biomed.scripts.run_server:app --host 0.0.0.0 --port 8082
python -m uvicorn open_biomed.scripts.run_server_workflow:app --host 0.0.0.0 --port 8083
Architecture
Core Directories
open_biomed/data/: Data structures forMolecule,Protein,Pocket,Cell,Textentitiesopen_biomed/models/: Model implementations organized by typefoundation_models/: BioT5, MolT5, PharmolixFM, BioMedGPT, etc.task_models/: Task-specific model wrappersprotein/,molecule/,cell/: Domain-specific models
open_biomed/tasks/: Task definitionsaidd_tasks/: Drug discovery tasks (property prediction, docking, drug design)multi_modal_tasks/: QA, captioning, translation tasks
open_biomed/tools/: Tool implementations (visualization, web requests, property calculators)open_biomed/core/: Infrastructurepipeline.py:TrainValPipeline,InferencePipelinefor training/inferenceworkflow.py: DAG-based workflow executionagent.py: LangGraph-based LLM agent system
configs/: YAML configurations for models, datasets, workflows, visualization
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.
- 9d ago First seen · 160 lines · 1,523 tokens per session scan A 78a23f87f328
OpenBioMed CLAUDE.md is an instructions file published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 1,523 tokens to every session, about $0.0076 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.
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