DrugClaw

DrugClaw is an AI Research Assistant for Accelerated Drug Discovery, implemented as a Rust multi-channel agent runtime. One agent core serves chat channels, the local Web UI, hooks, scheduled tasks, and domain skills without splitting the product into separate bots.

TL;DR

DrugClaw is a multi-channel AI runtime focused on drug-discovery research workflows.
It combines tool use, memory, skills, scheduler, hooks, and Web UI in one agent core.
You can run it locally with drugclaw setup, drugclaw doctor, and drugclaw start.
Treat docking/QSAR/ADMET outputs as prioritization signals, not experimental proof.

Built with Rust. 🦀

This project is built on top of microclaw.

What DrugClaw Is

DrugClaw combines drug-discovery research workflows with a general-purpose agent runtime:

a channel-agnostic agent loop with tool use and session resume
a provider-agnostic LLM layer with Anthropic and OpenAI-compatible backends
persistent SQLite storage for chats, memory, auth, and observability
a local Web UI and HTTP hook surface for operations and automation
a skills system for reproducible workflows, including bioinformatics, chemistry, and docking

Current channel adapters include:

Telegram
Discord
Slack
Feishu / Lark
Matrix
WhatsApp Cloud API
IRC
iMessage
Email
Nostr
Signal
DingTalk
QQ
Web

Current Scope

DrugClaw is already useful as:

a tool-using chat agent for code and file operations
a multi-chat automation runtime with hooks and scheduled tasks
a memory-backed assistant with file memory plus structured SQLite memory
a local operator console through the Web UI
a research assistant for literature review, public database triage, molecular property analysis, DrugBank retrieval, QSAR, and docking workflows

The runtime is generic enough to automate other workflows, but the product direction is explicitly drug-discovery research acceleration.

Capability Boundary

DrugClaw is strong at:

literature and public-database lookup
structured note-taking over biological and chemical artifacts
reproducible scripting for bioinformatics and computational chemistry
heuristic prioritization through docking, ADMET triage, QSAR, and structure-aware scoring
moving from chat intent to saved artifacts, reports, and follow-up analyses

DrugClaw is not:

a wet-lab automation system
a substitute for medicinal chemistry or structural biology judgment
a clinically validated ADMET or affinity oracle
a regulatory, diagnostic, or treatment decision system
proof that a compound works in vitro, in vivo, or in humans

When DrugClaw reports docking scores, QSAR predictions, ADMET heuristics, or affinity estimates, those outputs should be treated as prioritization signals only.

Prerequisites

macOS or Linux
Docker Desktop
Anthropic API key

Demo Examples

Below are live demonstrations of DrugClaw handling real tasks via Telegram.

Show Demo Examples

Protein Structure Rendering

Fetch a PDB structure, render it in rainbow coloring with PyMOL, and send the image.

PubMed Literature Search

Search PubMed for recent high-impact papers and provide structured summaries.

Hydrogen Bond Analysis

Visualize hydrogen bonds between a ligand and protein in PDB 3BIK.

Target Intelligence Dossier

Build a concise target dossier by combining UniProt, OpenTargets, Reactome, STRING, ClinVar, and known-drug evidence into one brief.

Compound Database Triage

Query PubChem, ChEMBL, and BindingDB for a compound or target, normalize the returned activity records, and send back a ranked summary table.

Docking Workflow Summary

Generate the search box, run docking, and return the top poses with a compact report.

Install

One-line installer

curl -fsSL https://drugclaw.com/install.sh | bash

When Docker is installed and the daemon is reachable, the installer also tries to build the default science sandbox image drugclaw-drug-sandbox:latest.

Windows PowerShell installer

iwr https://drugclaw.com/install.ps1 -UseBasicParsing | iex

From source

git clone https://github.com/DrugClaw/DrugClaw.git
cd drugclaw
cargo build
npm --prefix web install
npm --prefix web run build

Uninstall

./uninstall.sh

Quick Start

1. Create config

cp drugclaw.config.example.yaml drugclaw.config.yaml

2. Run setup and diagnostics

drugclaw setup
drugclaw doctor

If the default sandbox image is already present locally, drugclaw setup defaults the bash sandbox to enabled.

3. Start runtime

drugclaw start

4. Open the local Web UI

By default the UI listens on http://127.0.0.1:10961.

Minimal Config

A smallest practical config is usually Web-first, then add channels as needed.

llm_provider: "anthropic"
api_key: "replace-me"
model: ""

data_dir: "./drugclaw.data"
working_dir: "./tmp"
working_dir_isolation: "chat"

channels:
  web:
    enabled: true
  telegram:
    enabled: false

web_host: "127.0.0.1"
web_port: 10961

Recommended next steps:

enable one chat channel under channels:
set soul_path or add SOUL.md
enable sandboxing for code execution when you need stronger isolation
use drugclaw web password-generate for Web operator access

Core Concepts

Agent loop

The runtime does one thing consistently across channels:

load chat state and memory
build the system prompt plus skills catalog
call the selected model with tool schemas
execute tools when requested
persist the updated session and artifacts

The shared loop lives in src/agent_engine.rs. Channels are ingress and egress adapters, not separate agent implementations.

Memory

DrugClaw has two memory layers:

file memory: AGENTS.md plus chat-scoped files under runtime/groups/
structured memory: SQLite-backed facts, confidence, supersession, and observability

This lets the runtime keep durable context without forcing every instruction into a single prompt.

Skills

Show Skills Overview

Bundled skills currently include:

bio-tools
bio-db-tools
bayesian-optimization-tools
omics-tools
grn-tools
target-intelligence-tools
variant-analysis-tools
pharma-db-tools
chem-tools
pharma-ml-tools
literature-review-tools
medical-data-tools
clinical-research-tools
medical-qms-tools
stat-modeling-tools
survival-analysis-tools
scientific-visualization-tools
scientific-workflow-tools
docking-tools
document, spreadsheet, PDF, GitHub, weather, and macOS utility skills

Bundled domain skills now cover:

sequence analysis and general bioinformatics workflows
public biology database lookup across UniProt, PDB, AlphaFold, ClinVar, dbSNP, gnomAD, Ensembl, GEO, InterPro, KEGG, OpenTargets, Reactome, and STRING
AnnData, single-cell, BAM or CRAM, and mzML dataset triage
Arboreto-based gene regulatory network inference with GRNBoost2 or GENIE3
local VCF, SNV, indel, and SV summarization plus target-intelligence dossiers
public drug-discovery database lookup across PubChem, ChEMBL, BindingDB, openFDA, ClinicalTrials.gov, and OpenAlex
datamol, molfeat, PyTDC, and medchem-backed pharma ML preparation
DeepChem, RDKit, PySCF, assay normalization, QSAR, virtual screening, and DrugBank lookup
hypothesis tests, statsmodels regression, Kaplan-Meier, Cox modeling, and reusable scientific figures
citation cleanup, evidence matrices, hypothesis framing, and reproducibility checklists
Bayesian optimization for bounded experiment suggestion and parameter tuning
DICOM metadata inspection, biosignal analysis, and cohort-table profiling for medical research datasets
clinical-research design, reporting-guideline selection, and study-planning support
Vina-based docking plus downstream chemistry post-processing

See docs/operations/science-runtime.md for runtime requirements.

Hooks

Hooks let you gate or modify LLM and tool traffic at runtime.

Supported events:

BeforeLLMCall
BeforeToolCall
AfterToolCall

Supported outcomes:

allow
block
modify

See docs/hooks/HOOK.md.

ClawHub

ClawHub is the registry layer for discovering and installing skills.

Use:

drugclaw skill search <query>
drugclaw skill install <slug>
drugclaw skill list

Reference: docs/clawhub/overview.md

Web UI And Hooks

The local Web surface is not an afterthought. It exposes:

session and history browsing across channels
auth and API key management
metrics and memory observability
config self-check and runtime operations
HTTP hook endpoints for automation ingress

Important endpoints:

POST /hooks/agent
POST /api/hooks/agent
POST /hooks/wake
POST /api/hooks/wake

Reference: docs/operations/http-hook-trigger.md

Science Skills

DrugClaw now ships a non-trivial scientific workflow layer.

Show Science Skills Details

`bio-tools`

Use for:

FASTA / FASTQ / BAM / BED workflows
BLAST, alignment, QC, plotting, structure rendering
literature search and general bioinformatics scripting

`bio-db-tools`

Use for API-backed lookup of:

UniProt
RCSB PDB
AlphaFold DB
ClinVar
dbSNP
gnomAD
Ensembl
GEO
InterPro
KEGG
OpenTargets
Reactome
STRING

Bundled template:

skills/science/bio-db-tools/templates/bio_db_lookup.py

`omics-tools`

Use for:

h5ad and AnnData triage before Scanpy or scvi workflows
BAM or CRAM region inspection with pysam
mzML experiment inventory before pyOpenMS workflows

Bundled templates:

skills/science/omics-tools/templates/single_cell_profile.py
skills/science/omics-tools/templates/pysam_region_profile.py
skills/science/omics-tools/templates/mzml_summary.py

`grn-tools`

Use for:

GRNBoost2 or GENIE3 regulatory-edge inference
transcription factor to target ranking from bulk or single-cell expression matrices
TF-whitelist constrained GRN runs with Arboreto

Bundled template:

skills/genomics/grn-tools/templates/arboreto_grn.py

`variant-analysis-tools`

Use for:

local VCF or BCF summarization
VAF, depth, PASS, and consequence filtering
SNV, indel, and SV mutation-class counts before downstream annotation

Bundled template:

skills/genomics/variant-analysis-tools/templates/variant_report.py

`target-intelligence-tools`

Use for:

one-file target briefs spanning identifiers, disease evidence, known drugs, pathways, and interaction partners
compact target-validation snapshots before screening or docking
integrating UniProt, OpenTargets, STRING, Reactome, ClinVar, and gnomAD signals into one dossier

Bundled template:

skills/research/target-intelligence-tools/templates/target_dossier.py

`pharma-db-tools`

Use for API-backed lookup of:

PubChem
ChEMBL
BindingDB measured affinities
openFDA drug labels, events, NDC, recalls, approvals, and shortages
ClinicalTrials.gov
OpenAlex

Bundled template:

skills/pharma/pharma-db-tools/templates/pharma_db_lookup.py

`chem-tools`

Use for:

DeepChem featurization
RDKit descriptors
heuristic ADMET screening
DrugBank local or online lookup
assay-table normalization
QSAR and bioactivity prediction
ligand-only and structure-aware affinity prediction
virtual screening reranking

`pharma-ml-tools`

Use for:

datamol-backed library profiling and scaffold summaries
molfeat feature generation for QSAR or ranking workflows
PyTDC benchmark dataset fetch and split export
medchem rule and alert screening before prioritization

Bundled templates:

skills/pharma/pharma-ml-tools/templates/datamol_library_profile.py
skills/pharma/pharma-ml-tools/templates/molfeat_featurize.py
skills/pharma/pharma-ml-tools/templates/pytdc_dataset_fetch.py
skills/pharma/pharma-ml-tools/templates/medchem_screen.py

`literature-review-tools`

Use for:

citation-table normalization and deduplication
lightweight BibTeX export from local metadata tables
evidence-matrix assembly for review writing or gap mapping

Bundled templates:

skills/science/literature-review-tools/templates/citation_table_normalize.py
skills/science/literature-review-tools/templates/evidence_matrix.py

`medical-data-tools`

Use for:

DICOM metadata inspection and basic de-identification
ECG, PPG, EDA, RSP, or EMG analysis with NeuroKit2
cohort-table profiling for clinical research datasets

Bundled templates:

skills/medical/medical-data-tools/templates/dicom_inspect.py
skills/medical/medical-data-tools/templates/neuro_signal_analyze.py
skills/medical/medical-data-tools/templates/clinical_cohort_profile.py

`clinical-research-tools`

Use for:

study design and endpoint planning
reporting-guideline routing such as CONSORT, STROBE, PRISMA, STARD, TRIPOD, SPIRIT, and ICH E3
protocol, SAP, and evidence-synthesis support
bias, confounding, and sample-size review

`medical-qms-tools`

Use for:

ISO 13485 or FDA QMSR documentation planning
gap assessment for QMS procedures and records
CAPA, complaint, audit, supplier, and design-control documentation review

`stat-modeling-tools`

Use for:

statistical hypothesis tests with explicit result export
OLS, logistic, and Poisson regression with statsmodels
coefficient tables, confidence intervals, and model-fit summaries

Bundled templates:

skills/science/stat-modeling-tools/templates/stat_test_report.py
skills/science/stat-modeling-tools/templates/statsmodels_regression.py

`survival-analysis-tools`

Use for:

Kaplan-Meier summaries and plots
log-rank comparisons between groups
Cox proportional hazards baselines with hazard-ratio export

Bundled template:

skills/science/survival-analysis-tools/templates/survival_analysis.py

`scientific-visualization-tools`

Use for:

static publication-style figures with seaborn or matplotlib
interactive Plotly HTML charts for exploratory research review
reusable plotting scripts parameterized by column names

Bundled templates:

skills/science/scientific-visualization-tools/templates/publication_plot.py
skills/science/scientific-visualization-tools/templates/interactive_plot.py

`scientific-workflow-tools`

Use for:

hypothesis framing from observations
peer-review style critique for rigor and claim scope
reproducibility checklist generation
research-method planning and scientific-writing structure

Bundled template:

skills/science/scientific-workflow-tools/templates/reproducibility_checklist.py

`bayesian-optimization-tools`

Use for:

proposing the next experiment from bounded numeric parameters
balancing exploration and exploitation over expensive assay or reaction runs
tuning conditions or hyperparameters with Gaussian-process surrogates

Bundled template:

skills/research/bayesian-optimization-tools/templates/bayesian_optimize.py

`docking-tools`

Use for:

receptor / ligand preparation
AutoDock Vina workflows
box generation
PyMOL rendering
docking reports and chemistry post-processing

Bundled templates:

skills/pharma/docking-tools/templates/docking_workflow.py
skills/pharma/docking-tools/templates/docking_manifest.example.json

Sandbox images

Build the bundled runtime image when you need the full scientific and docking toolchain:

docker build -f docker/drug-sandbox.Dockerfile -t drugclaw-drug-sandbox:latest .

Those Dockerfiles read version-constrained requirements from docker/requirements-*.txt so the science stack does not drift on every rebuild.

Design choice:

drug-sandbox is the canonical shared runtime for bio, omics, chemistry, literature, medical-research, and docking skills
the legacy drug-sandbox-docking tag is retained only as a compatibility alias for older configs and scripts
dedicated med-sandbox or chem-sandbox images are not provided because the current dependency overlap is high and the operator complexity is not worth it

Reference: docs/operations/science-runtime.md

Configuration Model

The main config file is drugclaw.config.yaml.

Important fields:

llm_provider, api_key, model
data_dir, working_dir, working_dir_isolation
channels.*
sandbox.*
soul_path, souls_dir
plugins.*
clawhub_*
voice_provider, voice_transcription_command
web_host, web_port

Useful facts:

channels: is the preferred configuration surface for modern channel setup.
SOUL.md can be global, channel-level, or account-level.
built-in skills are installed under <data_dir>/skills unless skills_dir overrides that path.
runtime env overrides still use MICROCLAW_* prefixes for compatibility, including MICROCLAW_CONFIG and MICROCLAW_SKILLS_DIR.

Start from drugclaw.config.example.yaml.

CLI

Main commands:

drugclaw start
drugclaw acp
drugclaw setup
drugclaw doctor
drugclaw gateway
drugclaw skill
drugclaw hooks
drugclaw web
drugclaw reembed
drugclaw upgrade
drugclaw version

Examples:

drugclaw web password-generate
drugclaw hooks list
drugclaw gateway status
drugclaw skill search docking

ACP stdio mode (optional)

DrugClaw can run as an Agent Client Protocol (ACP) server over stdio:

drugclaw acp

Use this mode when another local tool needs a sessioned DrugClaw runtime over stdio instead of Telegram/Discord/Web ingress.

Shell Scripts

The repository keeps operator scripts aligned with the runtime:

install.sh: install the latest release binary on macOS or Linux
uninstall.sh: remove the installed binary
start.sh: build the Web UI and run the local runtime from source
check.sh: run the standard local validation set
deploy.sh: run release automation and optional nixpkgs follow-up
scripts/test_http_hooks.sh: smoke-test Web hook endpoints
scripts/matrix-smoke-test.sh: end-to-end Matrix ingestion smoke test
scripts/update-nixpkgs.sh: update the drugclaw package in nixpkgs

These scripts are repository workflow glue, not a second control plane.

Development

Build

cargo build
npm --prefix web run build
npm --prefix website run build

Test

cargo test
./check.sh

Docs drift guard

node scripts/generate_docs_artifacts.mjs --check

Architecture

Key files:

src/main.rs: CLI entrypoint
src/runtime.rs: application wiring
src/agent_engine.rs: shared agent loop
src/llm.rs: provider abstraction
src/web.rs: Web router and APIs
src/scheduler.rs: scheduled tasks and reflector loop
src/skills.rs: skill discovery and activation
src/mcp.rs: MCP integration
src/hooks.rs: runtime hook system

Supporting crates:

drugclaw-core
drugclaw-storage
drugclaw-tools
drugclaw-channels
drugclaw-app

Name		Name	Last commit message	Last commit date
Latest commit History 19 Commits
.github/workflows		.github/workflows
crates		crates
docker		docker
docs		docs
examples/plugins		examples/plugins
gui		gui
hooks		hooks
screenshots		screenshots
scripts		scripts
skills		skills
src		src
tests		tests
web		web
.dockerignore		.dockerignore
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
Cargo.lock		Cargo.lock
Cargo.toml		Cargo.toml
DEVELOP.md		DEVELOP.md
Dockerfile		Dockerfile
LICENSE		LICENSE
NIXPKGS_README.md		NIXPKGS_README.md
README.md		README.md
README_CN.md		README_CN.md
SOUL.md		SOUL.md
TEST.md		TEST.md
build.rs		build.rs
check.sh		check.sh
deploy.sh		deploy.sh
docker-compose.yaml		docker-compose.yaml
drugclaw.config.example.yaml		drugclaw.config.example.yaml
flake.lock		flake.lock
flake.nix		flake.nix
icon.png		icon.png
install.ps1		install.ps1
install.sh		install.sh
mcp.example.json		mcp.example.json
mcp.hapi-bridge.example.json		mcp.hapi-bridge.example.json
mcp.minimal.example.json		mcp.minimal.example.json
mcp.peekaboo.example.json		mcp.peekaboo.example.json
mcp.windows.desktop.example.json		mcp.windows.desktop.example.json
rust-toolchain.toml		rust-toolchain.toml
start.sh		start.sh
uninstall.ps1		uninstall.ps1
uninstall.sh		uninstall.sh

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DrugClaw

TL;DR

Table of Contents

Built with Rust. 🦀

What DrugClaw Is

Current Scope

Capability Boundary

Prerequisites

Demo Examples

Install

One-line installer

Windows PowerShell installer

From source

Uninstall

Quick Start

1. Create config

2. Run setup and diagnostics

3. Start runtime

4. Open the local Web UI

Minimal Config

Core Concepts

Agent loop

Memory

Skills

Hooks

ClawHub

Web UI And Hooks

Science Skills

bio-tools

bio-db-tools

omics-tools

grn-tools

variant-analysis-tools

target-intelligence-tools

pharma-db-tools

chem-tools

pharma-ml-tools

literature-review-tools

medical-data-tools

clinical-research-tools

medical-qms-tools

stat-modeling-tools

survival-analysis-tools

scientific-visualization-tools

scientific-workflow-tools

bayesian-optimization-tools

docking-tools

Sandbox images

Configuration Model

CLI

ACP stdio mode (optional)

Shell Scripts

Development

Build

Test

Docs drift guard

Architecture

Documentation Map

License

Star History

About

Topics

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases 2

Packages 0

Uh oh!

Contributors

Uh oh!

Languages

`bio-tools`

`bio-db-tools`

`omics-tools`

`grn-tools`

`variant-analysis-tools`

`target-intelligence-tools`

`pharma-db-tools`

`chem-tools`

`pharma-ml-tools`

`literature-review-tools`

`medical-data-tools`

`clinical-research-tools`

`medical-qms-tools`

`stat-modeling-tools`

`survival-analysis-tools`

`scientific-visualization-tools`

`scientific-workflow-tools`

`bayesian-optimization-tools`

`docking-tools`

Packages