README.md

metadata

license: mit
library_name: pytorch
pipeline_tag: feature-extraction
tags:
  - protein
  - structural-biology
  - representation-learning
  - 3d-cnn
  - foldvision

FoldVision Encoder

Model Summary

FoldVision is a protein 3D-CNN encoder that maps a voxelized protein structure to a fixed-size embedding (1024 dimensions).

Primary task:

• Protein feature extraction from 3D structure.

Typical downstream tasks (with finetuning heads):

• Protein-only regression/classification.
• PSI (protein-small molecule interactions) prediction when combined with a SMILES encoder.

GitHub code: foldvision_github

Model Details

• Model name: AlexanderKroll/foldvision-encoder
• Architecture: 3D CNN encoder with GroupNorm blocks and global pooling.
• Framework: PyTorch
• Input channels: 5 atom-type channels (C, N, S, O, P)
• Output: (B, 1024) embedding

Input and Preprocessing

This model expects FoldVision voxel tensors generated from PDB structures.

Recommended preprocessing pipeline:

1. Convert .pdb files to sparse point lists (numpy_3D_point_lists/*.npz).
2. Use bounding_boxes.npy + dataloader to construct dense tensors at runtime.

Repository scripts:

• scripts/preprocess_pdb_dir.py
• scripts/embed_proteins.py
• scripts/train.py
• scripts/train_PSI.py
• scripts/evaluate.py
• scripts/evaluate_PSI.py

Usage

from foldvision import FoldVisionEncoder

model = FoldVisionEncoder.from_pretrained("AlexanderKroll/foldvision-encoder")
model.eval()
# x: (B, 5, Z, Y, X)
# z = model(x)  # (B, 1024)

Multi-Run Embeddings and Predictions

FoldVision pipelines support repeated runs with random 3D rotations (test-time augmentation).

• Embeddings:
  • per-run: keep each run-specific embedding,
  • aggregated: use mean embedding for a stable representation.
• Predictions:
  • per-run predictions can be used to inspect spread/uncertainty,
  • averaged predictions are recommended for reporting.

Citation

If you use this model, cite:

1. FoldVision bioRxiv manuscript:

@article{foldvision_biorxiv,
  title   = {FoldVision: A compute-efficient atom-level 3D protein encoder},
  author  = {Kroll, Alexander and Yadav, Shantanu and Lercher, Martin J.},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.01.23.701326},
  url     = {https://doi.org/10.64898/2026.01.23.701326}
}

2. The GitHub repository:

@misc{foldvision_github,
  title        = {FoldVision code repository},
  author       = {Kroll, Alexander},
  year         = {2026},
  howpublished = {\url{https://github.com/AlexanderKroll/foldvision}}
}

Model Card Contact

For issues or questions, use the GitHub issue tracker in the FoldVision repository.