README.md

metadata

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. The code for the paper and model is available at: 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 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.

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Dataset Statistics

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

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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

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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:

1. Download and extract the original PDB files:

   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/.

   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), then run:

   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), you can sample macrocyclic peptides. First, ensure you have downloaded the pre-trained checkpoints (e.g., nf_unified and fb_unified) from Hugging Face and placed them in the appropriate exps/ directories within the FuncBind repository.

   Then, from the FuncBind root directory, run:

   python sample_fb.py --config-name sample_fb_mcpp