Add README.md

README.md

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 ---
-title: Rascore Api
-emoji: 📉
-colorFrom: green
-colorTo: green
+title: RAScore API
 sdk: docker
+app_port: 7860
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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](https://doi.org/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".