---
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**.

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**.

---

## Dataset Statistics
- MCP lengths: **4–25 amino acids** (average 10)  
- **78%** of MCPs contain one or more **non-canonical amino acids**  

---

## 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`  |\

---

## 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.

    Then, from the FuncBind root directory, run:
    ```bash
    python sample_fb.py --config-name sample_fb_mcpp
    ```