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Predicting properties of small molecules using MPNN on QM9 dataset

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Shreyas-Bhat/MolPropPred

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MolPropPred

Clone

This repo uses git submodules for the MolPropPredTool. Therefore, you need to clone the repository with --recurse-submodules.

git clone --recurse-submodules https://github.com/Shreyas-Bhat/MolPropPred

In case you forget to do that, you need to run the following command:

git submodule update --init

The tool can be accessed in MolPropPredTool folder if cloned using git or downloaded zip from the repo

MolPropPredTool V0.1

Prerequisites

  • Python 3.10
  • Python Dependencies :
    • numpy
    • deepchem
    • matplotlib==3.3.4
    • pandas==1.1.5
    • rdkit_pypi
    • scikit_learn
    • torch
    • tensorflow

Installation :

It is recommended to use a Virtual Environment to install the dependencies. You may require administrator privileges to install the Virtual Environment. Installation instructions have been tested on Ubuntu, but should also work on other Linux based operating systems and MacOS.

  1. Install virtualenv with pip install virtualenv.
  2. Create the virtual environment with virtualenv venv.
  3. Activate the virtual environment with source venv/bin/activate.
  4. Install the dependencies in the virtual environment with pip install -r requirements.txt.
  5. Type deactivate to deactivate the Virutal Environment once you are done.

or run the following

pip install virtualenv
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

MolPropPredTool Usage :

python3 main.py <SMILE-string>

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Predicting properties of small molecules using MPNN on QM9 dataset

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