## Geometric Vector Perceptron

Implementation of Geometric Vector Perceptron, a simple circuit with 3d rotation equivariance for learning over large biomolecules, in Pytorch. The repository may also contain experimentation to see if this could be easily extended to self-attention.

## Install

`$ pip install geometric-vector-perceptron`

### Functionality

`GVP`

: Implementing the basic geometric vector perceptron.`GVPDropout`

: Adapted dropout for GVP in MPNN context`GVPLayerNorm`

: Adapted LayerNorm for GVP in MPNN context`GVP_MPNN`

: Adapted instance of Message Passing class from`torch-geometric`

package. Still not tested.

## Usage

```
import torch
from geometric_vector_perceptron import GVP
model = GVP(
dim_vectors_in = 1024,
dim_feats_in = 512,
dim_vectors_out = 256,
dim_feats_out = 512
)
feats, vectors = (torch.randn(1, 512), torch.randn(1, 1024, 3))
feats_out, vectors_out = model( (feats, vectors) ) # (1, 256), (1, 512, 3)
```

With the specialized dropout and layernorm as described in the paper

```
import torch
from torch import nn
from geometric_vector_perceptron import GVP, GVPDropout, GVPLayerNorm
model = GVP(
dim_vectors_in = 1024,
dim_feats_in = 512,
dim_vectors_out = 256,
dim_feats_out = 512
)
dropout = GVPDropout(0.2)
norm = GVPLayerNorm(512)
feats, vectors = (torch.randn(1, 512), torch.randn(1, 1024, 3))
feats, vectors = model( (feats, vectors) )
feats, vectors = dropout(feats, vectors)
feats, vectors = norm(feats, vectors) # (1, 256), (1, 512, 3)
```

#### TF implementation:

The original implementation in TF by the paper authors can be found here: https://github.com/drorlab/gvp/

## Citations

```
@inproceedings{
anonymous2021learning,
title={Learning from Protein Structure with Geometric Vector Perceptrons},
author={Anonymous},
booktitle={Submitted to International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=1YLJDvSx6J4},
note={under review}
}
```