| # Unified All-Atom Molecule Generation with Neural Fields — MCPP Dataset | |
| 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: | |
| We used this datase in FuncBind (see https://huggingface.co/papers/2511.15906). | |
| ## 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` | | |
| --- | |
| ## How to Use | |
| 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/ |