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--- |
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license: apache-2.0 |
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pipeline_tag: graph-ml |
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--- |
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# Unified all-atom molecule generation with neural fields |
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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. |
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Code: [https://github.com/prescient-design/funcbind](https://github.com/prescient-design/funcbind) |
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This repository provides model weights for FuncBind and preprocessed datasets (train/test CrossDocked). |
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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). |
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For detailed instructions on installation, data preparation, sampling, and training, please refer to the comprehensive [GitHub repository](https://github.com/prescient-design/funcbind). |
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## Sample Usage |
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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: |
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```bash |
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python sample_fb.py --config-name sample_fb_mcpp |
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``` |
