---
license: apache-2.0
pipeline_tag: graph-ml
---

# Unified all-atom molecule generation with neural fields

This repository contains the model weights for **FuncBind**, a framework for target-conditioned 3D molecule generation using neural fields. As presented in the paper [Unified all-atom molecule generation with neural fields](https://huggingface.co/papers/2511.15906), this unified model leverages score-based generative models and neural fields to represent molecules as continuous atomic densities, enabling it to be trained across diverse atomic systems and drug modalities.

Code: [https://github.com/prescient-design/funcbind](https://github.com/prescient-design/funcbind)

This repository provides model weights for FuncBind and preprocessed datasets (train/test CrossDocked).
The Macrocyclic Peptide Pair (MCP) dataset and its preprocessed splits are available at [https://huggingface.co/datasets/Willete3/mcpp_dataset](https://huggingface.co/datasets/Willete3/mcpp_dataset).

For detailed instructions on installation, data preparation, sampling, and training, please refer to the comprehensive [GitHub repository](https://github.com/prescient-design/funcbind).

## Sample Usage

After setting up the environment and downloading the necessary checkpoints as outlined in the [GitHub repository](https://github.com/prescient-design/funcbind), you can sample macrocyclic peptides (MCPs) from the model using the following command:

```bash
python sample_fb.py --config-name sample_fb_mcpp
```