Use the pretrained model:

Train the model:

Table of Contents

• Table of Contents
• Installation
• Use pretrained model for prediction
• Train the model

BonDNet is a graph neural network model for the prediction of bond dissociation energies (BDEs). It can be applied to both homolytic and heterolytic bond dissociations for molecules of any charge. This model is described in the paper: BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules, Chemical Science, 2021.

Installation

Currently, we support installation from source:

1. create a conda environment

   conda create --name bondnet
   conda activate bondnet
   conda install python

2. install dependencies

   conda install "pytorch>=1.10.0" -c pytorch
   conda install "dgl>=0.5.0" -c dglteam
   conda install "pymatgen>=2022.01.08" "rdkit>=2020.03.5" "openbabel>=3.1.1" -c conda-forge

3. install this repo

   git clone https://github.com/mjwen/bondnet.git
   pip install -e bondnet

Use pretrained model for prediction

The predicted BDE will be in the units of eV.

For a quick prediction of the BDEs for a single molecule, try the live demo at:

Alternatively, a command line interface (CLI) bondnet is provided for batch predictions.

• A single molecule given by a SMILES or InChI string, e.g.:

  bondnet single "C1COC(=O)O1"

• Multiple molecules listed in a file. Supported molecule format includes sdf, pdb, smiles and inchi, e.g.:

  bondnet multiple molecules.sdf -o results.sdf

• Explicitly specifying the bond dissociation reactions. In this mode, a moleclue file listing all the molecules and a reaction file listing all the bond dissociation reactions are needed. Supported molecule format includes graph , sdf, pdb, smiles , and inchi. e.g.

  bondnet reaction -t graph molecule_graphs.json reactions.csv
  bondnet reaction -t sdf molecules.sdf reactions.csv

More information

• More detailed instructions, example input files, and description of the file formats, can be found here.
• To get help, do bondnet -h. Help of subcommand can be obtained by, e.g. bondnet single -h

Train the model

A new implementation of BondNet can be found here, which trains ~4 times faster. But the below instructions should still work.

The train_bde.ipynb Jupyter notebook shows how to train BonDNet on a BDE dataset of both neutral and charged molecules. Try it at:

The train_bde.ipynb Jupyter notebook trains a model on CPU. If you want to train on GPUs (a single GPU or distributed), take a look at train_bde_distributed.py. A model can be trained by

python train_bde_distributed.py  molecules.sdf  molecule_attributes.yaml  reactions.yaml

More detailed instructions, example input files, and description of the file formats, can be found here.