SEA Web Server

A web application for running Similarity Ensemble Approach (SEA) searches and viewing results. SEA relates proteins based on the set-wise chemical similarity among their ligands and can be used to rapidly search large compound databases and build cross-target similarity maps.

Provided by the Shoichet Laboratory in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSF).

Architecture

Service	Description
web	FastAPI backend serving the API and (in production) the built React frontend
redis	Message broker and result backend for Celery
redisworker	Celery worker processing SEA scoring tasks
redisbeat	Celery beat scheduler for periodic tasks
frontend	Vite dev server with HMR (development only)

Prerequisites

• Docker and Docker Compose
• Access to the SEA data folder and job directory (NFS or local paths)

Environment Setup

Create a .env file in the project root:

REDIS_HOST=redis
REDIS_PORT=6380

CELERY_BROKER_URL="redis://redis:6380/0"
CELERY_RESULT_BACKEND="redis://redis:6380/1"
MAX_QUEUE_SIZE=50

SEA_DATA_FOLDER="/path/to/sea_data"
SEA_JOB_DIR="/path/to/sea_jobs"

# SEA parameters
TC_CUTOFF=0.29
MEAN_PARA=4.24 * 10 ** -4
STD_COEF=4.49 * 10 ** -3
STD_EXP=0.665
RADIUS=2
FP_SIZE=2048
USE_FEATUREs=False

# ALTCHA captcha HMAC secret (generate your own)
ALTCHA_HMAC_SECRET="your-secret-here"

Running with Docker

Development

Uses Dockerfile.dev with a separate Vite dev server for frontend HMR, source-mounted backend for live reload, and lower worker concurrency.

docker compose -f compose.dev.yml up --build

• Frontend (Vite HMR): http://localhost:5173
• Backend API: http://localhost:5055

Production

Uses Dockerfile.prod with a multi-stage build that compiles the React frontend and bundles it into the backend image.

docker compose -f compose.prod.yml up --build -d

• Application: http://localhost:5050

Running without Docker

1. Set up the Conda environment

conda env create -f environment.yml
conda activate sea25
pip install -r requirements.txt

2. Start Redis

redis-server --port 6380 --daemonize yes

3. Start the backend and worker (in separate terminals)

uvicorn backend.app.api:app --host 0.0.0.0 --port 5050 --reload

celery -A backend.app.celery_worker worker --beat -s /tmp/sea-celerybeat-schedule --loglevel=info --concurrency=5 --max-tasks-per-child=10

4. Start the frontend dev server

cd frontend
npm install
npm run dev