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Deep_Chem_Tutorial

Sample notebooks for Deep Chem library (Application of Graph Neural Networks in Material Informatics)

This repository provides several notebooks as tutorial for Deep Chem, including:

  • Basics
  • Datasets
  • How to get graph features
  • How to use PyTorch & Keras models
  • Cross-validation & hyperparameter tuning

I have used several graph models including:

  • GraphConvModel
  • WeaveModel
  • GATModel
  • GCNModel
  • AttentiveFPModel

Deep Chem

DeepChem is an open source library for use of deep-learning in drug discovery, materials science, quantum chemistry, and biology.

Deep Chem sorce code (Link)

Dependencies:

  • Python: 3.7.11
  • DeepChem: 2.5.0
  • TensorFlow: 2.6.0
  • tensorflow_probability: 0.14.0
  • PyTorch: 1.9.1
  • RDKit: 2021.03.5
  • scikit-learn: 0.23.2
  • pyGPGO: 0.5.1
  • DGL: 0.7.1
  • DGL-LifeSci: 0.2.8

Yaml environment

I created a Conda virtual environment named test_GNN, and installed main libraries. In order to make the same Conda environment, download the yaml (yaml) file. Then, run the following command in Terminal:

conda env create --file GNN_environment.yaml

Note: The current directory in Terminal should be the directory that the yaml file is stored.