Improve dataset card: Add metadata, links, and detailed sample usage

README.md

@@ -1,17 +1,34 @@
+---
+language:
+- en
+task_categories:
+- other
+tags:
+- chemistry
+- drug-discovery
+- molecule-generation
+- macrocyclic-peptide
+- 3d-generation
+- molecular-modeling
+---
+
 # Unified All-Atom Molecule Generation with Neural Fields — MCPP Dataset
 
+This repository contains the Macrocyclic Peptide Pair (MCPP) dataset, curated for the paper [Unified all-atom molecule generation with neural fields](https://huggingface.co/papers/2511.15906).
+The code for the paper and model is available at: [https://github.com/prescient-design/funcbind/](https://github.com/prescient-design/funcbind/)
+
 We curated a dataset of **186,685 MCP–protein complexes** (`mcpp_dataset.tar.gz`) starting from **641 protein–MCP complexes** from the **[RCSB PDB](https://www.rcsb.org/)** using a **“mutate-then-relax”** strategy:
 
 ## Dataset Generation Pipeline
 
-1. **Mutation:**  
-   MCPs were randomly mutated at **1 to 8 sites** using **213 distinct amino acids**.
+1.  **Mutation:**  
+    MCPs were randomly mutated at **1 to 8 sites** using **213 distinct amino acids**.
 
-2. **Relaxation:**  
-   Mutated complexes were relaxed using **FastRelax in Rosetta**, which iteratively performs side-chain packing and all-atom minimization.
+2.  **Relaxation:**  
+    Mutated complexes were relaxed using **FastRelax in Rosetta**, which iteratively performs side-chain packing and all-atom minimization.
 
-3. **Selection:**  
-   The best complexes were chosen based on **lowest interface scores**.
+3.  **Selection:**  
+    The best complexes were chosen based on **lowest interface scores**.
 
 ---
 
@@ -24,20 +41,45 @@ We curated a dataset of **186,685 MCP–protein complexes** (`mcpp_dataset.tar.g
 ## Dataset Splits
 The dataset is split using a clustering-based approach. The **test set** covers **100 protein pockets**:
 
-| Split         | File             |
-|---------------|----------------|
-| Training set  | `train_data.pt` |
-| Validation set| `val_data.pt`   |
-| Test set      | `test_data.pt`  |
+| Split          | File            |
+|----------------|-----------------|
+| Training set   | `train_data.pt` |
+| Validation set | `val_data.pt`   |
+| Test set       | `test_data.pt`  |\
 
 ---
 
-## How to Use
+## Sample Usage
+
+This dataset provides preprocessed `.pt` files (`train_data.pt`, `val_data.pt`, `test_data.pt`) and the original `.tar.gz` file containing `.pdb` files.
+
+To use this dataset with the [FuncBind codebase](https://github.com/prescient-design/funcbind/):
+
+1.  **Download and extract the original PDB files:**
+    ```bash
+    tar -xvzf mcpp_dataset.tar.gz
+    ```
+    This will create a `mcpp_dataset/` directory containing the PDB files.
+
+2.  **Place the preprocessed data:**
+    Copy the `.pt` files into the extracted `mcpp_dataset/` directory. If you have cloned the FuncBind repository, the target path would be `funcbind/dataset/data/mcpp_dataset/`.
+    ```bash
+    cp train_data.pt val_data.pt test_data.pt mcpp_dataset/
+    # Or if in FuncBind repo:
+    # cp train_data.pt val_data.pt test_data.pt funcbind/dataset/data/mcpp_dataset/
+    ```
+
+3.  **Alternatively, reprocess the data from scratch (within FuncBind repository):**
+    After downloading and untarring `mcpp_dataset.tar.gz` into `funcbind/dataset/data/mcpp_dataset/`, ensure you have set up the FuncBind environment (see [GitHub repository](https://github.com/prescient-design/funcbind/quick-start)), then run:
+    ```bash
+    cd funcbind/dataset
+    python preprocess_mcp_pair.py
+    ```
+
+4.  **Sample Macrocyclic Peptides with FuncBind:**
+    Once the data is prepared and FuncBind is installed (see [GitHub repository](https://github.com/prescient-design/funcbind/)), you can sample macrocyclic peptides. First, ensure you have downloaded the pre-trained checkpoints (e.g., `nf_unified` and `fb_unified`) from [Hugging Face](https://huggingface.co/mkirchmeyer/funcbind) and placed them in the appropriate `exps/` directories within the FuncBind repository.
 
-1. **Download and extract:**
-```bash
-tar -xvzf mcpp_dataset.tar.gz
-```
-2. **To generate MCP samples with Funcbind, :**
-```bash
-cp train_data.pt val_data.pt test_data.pt mcpp_dataset/
+    Then, from the FuncBind root directory, run:
+    ```bash
+    python sample_fb.py --config-name sample_fb_mcpp
+    ```