File size: 3,538 Bytes
60ed28e 0502414 60ed28e 0502414 60ed28e 0502414 60ed28e 0502414 60ed28e 0502414 60ed28e 0502414 60ed28e 0502414 60ed28e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
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
``` |