README.md

metadata

title: RAScore API
sdk: docker
app_port: 7860
pinned: false
license: mit

RAScore micro-service (isolated)

A small, CORS-enabled REST service that returns the Retrosynthetic Accessibility score (RAScore) for a list of SMILES. RAScore (Thakkar et al., Chem. Sci. 2021, 10.1039/D0SC05401A) is the probability that a computer-aided synthesis-planning tool (AiZynthFinder) can find a route to the molecule: 0 = hard / no route found, 1 = readily synthesizable. It is a fast, learned make-ability signal that complements the Ertl SA score.

Why this is a separate Space

RAScore's pretrained XGBoost model only unpickles under 2020-era pins (Python 3.7, scikit-learn 0.22.1, xgboost 1.0.2), which are incompatible with the modern ADMET-AI / Chemprop stack. Running it in its own container means it can never destabilise the ADMET endpoint. (The Dockerfile installs RAScore with --no-deps so it does not pull TensorFlow, which is only needed for the unused neural-net model.)

API

POST /score
Content-Type: application/json
{ "smiles": ["CCO", "c1ccccc1", ...] }      # up to 1000 per request

-> { "results": [ { "smiles": "CCO", "RAScore": 0.97 }, ... ] }

GET /health returns { "status": "ok" }.

Deploy

1. Create a new Hugging Face Space, SDK = Docker.
2. Add Dockerfile and app.py from this folder.
3. Wait for the build (first build is slow; the legacy pins are finicky — if it fails, check the logs, usually a numpy / scikit-learn ABI mismatch).
4. The Space serves at https://<user>-<space>.hf.space.

Local alternative: docker build -t rascore . && docker run -p 7860:7860 rascore.

Point MolParetoLab at it

In the app, click "Make-ability score (RAScore)" in the sidebar and paste your Space URL when prompted (stored in localStorage). The app fetches RAScore for every loaded molecule and exposes it as a Pareto objective (maximize) and an axis under "Make-ability & cost".