Update README.md

README.md

@@ -30,4 +30,82 @@ configs:
     path: data/valid-*
   - split: test
     path: data/test-*
+size_categories:
+- 1B<n<10B
 ---
+# ZINC_22 Pretraining Dataset
+
+## Dataset Description
+This dataset is derived from the **ZINC-22** database (~70B synthesizable compounds as of Sept 2024) and was prepared for large-scale pretraining of molecular language models. We randomly sampled **1.5 billion molecules** using a **stratified heavy-atom count split** (4–49 atoms) to ensure coverage of diverse chemical sizes.  
+All molecules were **deduplicated** to remove repeats, **canonicalized** in SMILES format, and **converted** into multiple string representations: SMILES, SELFIES, SAFE, DeepSMILES.  
+
+---
+
+## Precomputed Statistics
+This repository includes precomputed reference statistics (`*_stats.pkl`) for evaluating generated molecules against validation and test sets.  
+These statistics are used to compute the following metrics:
+
+- **FCD** – Fréchet ChemNet Distance  
+- **SNN** – Similarity to Nearest Neighbor  
+- **Frag** – Fragment similarity (BRICS decomposition)  
+- **Scaf** – Scaffold similarity (Bemis–Murcko scaffolds)  
+
+### File Naming Convention
+Files are provided for multiple reference set sizes:
+- `_175k` → 175,000 molecules  
+- `_500k` → 500,000 molecules  
+- `_1M` → 1 million molecules  
+- `_3M` → 3 million molecules  
+- *(no suffix)* → full set  
+
+By convention:
+- `valid_stats_*` → computed from the **random validation split**  
+- `test_stats_*` → computed from the **scaffold-based split**  
+
+These statistics enable **consistent and reproducible** evaluation across experiments.
+
+---
+
+## How to Use
+
+Before running the example below, make sure you have these packages installed:
+```bash
+pip install rdkit fcd-torch
+```
+### Example: Download stats from the Hub and compute FCD
+
+```python
+from huggingface_hub import hf_hub_download
+import pickle
+from fcd_torch import FCD as FCDMetric
+
+# 1. Download the precomputed stats file from Hugging Face Hub
+stats_path = hf_hub_download(
+    repo_id="chandar-lab/ZINC_22",
+    repo_type="dataset",
+    filename="valid_stats_175k.pkl"  # change to desired file
+)
+
+# 2. Load the reference stats
+with open(stats_path, "rb") as f:
+    reference_stats = pickle.load(f)
+
+# 3. Compute FCD for your generated molecules
+generated_smiles = ["CCO", "CCN", "CCCN", "CCCN"]  # replace with your generated set
+fcd_calculator = FCDMetric(batch_size=4)
+
+fcd_value = fcd_calculator(gen=generated_smiles, pref=reference_stats["FCD"])
+print(f"FCD score: {fcd_value:.4f}")
+```
+
+## Citation
+
+```bibtex
+@article{chitsaz2024novomolgen,
+  title={NovoMolGen: Rethinking Molecular Language Model Pretraining},
+  author={Chitsaz, Kamran and Balaji, Roshan and Fournier, Quentin and 
+          Bhatt, Nirav Pravinbhai and Chandar, Sarath},
+  journal={arXiv preprint},
+  year={2025},
+}
+```