mhg-gnn

This repository provides PyTorch source code assosiated with our publication, "MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural Network"

Paper: Arxiv Link

Introduction

We present MHG-GNN, an autoencoder architecture that has an encoder based on GNN and a decoder based on a sequential model with MHG. Since the encoder is a GNN variant, MHG-GNN can accept any molecule as input, and
demonstrate high predictive performance on molecular graph data. In addition, the decoder inherits the theoretical guarantee of MHG on always generating a structurally valid molecule as output.

Getting Started
1. Pretrained Models and Training Logs
2. Installation
Feature Extraction

Getting Started

This code and environment have been tested on Intel E5-2667 CPUs at 3.30GHz and NVIDIA A100 Tensor Core GPUs.

Pretrained Models and Training Logs

We provide checkpoints of the MHG-GNN model pre-trained on a dataset of ~1.34M molecules curated from PubChem. (later) For model weights: HuggingFace Link

Add the MHG-GNN pre-trained weights.pt to the models/ directory according to your needs.

Installation

We recommend to create a virtual environment. For example:

python3 -m venv .venv
. .venv/bin/activate

Type the following command once the virtual environment is activated:

git clone [email protected]:CMD-TRL/mhg-gnn.git
cd ./mhg-gnn
pip install .

Feature Extraction

The example notebook mhg-gnn_encoder_decoder_example.ipynb contains code to load checkpoint files and use the pre-trained model for encoder and decoder tasks.

To load mhg-gnn, you can simply use:

import torch
import load

model = load.load()

To encode SMILES into embeddings, you can use:

with torch.no_grad():
    repr = model.encode(["CCO", "O=C=O", "OC(=O)c1ccccc1C(=O)O"])

For decoder, you can use the function, so you can return from embeddings to SMILES strings:

orig = model.decode(repr)

Spaces:

ibm
/

FM4M-demo1

Running