Multidisciplinary Artificial Intelligence with Chemical Abstraction (MAICA)

This is a python package for machine learning with chemical data. It provides various pre-processing modules for chemical data, such as engineering conditions, chemical formulas, and molecular structures. Also, several wrapper classes and functions are included for chemical machine learning. This package was implemented based on Scikit-learn and PyTorch.


Before installing MAICA, several required packages should be installed in your environment. We highly recommend to use Anaconda to build your Python environment for MAICA.

  1. Install a cheminformatics package RDKit. RDKit is available at Anaconda archive. You can install RDKit using the following command in the Anaconda prompt.

>>> conda install -c rdkit rdkit
  1. Install a deep learning framework PyTorch. If you want to build your machine learning models using GPU, CUDA >= 11.1 must be installed your machine. With CUDA of version 11.1, you can install PyTorch using the following command.

>>> conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c conda-forge
  1. Install a graph-based deep learning framework PyTorch Geometric. It must be installed to build machine learning models that predict target values from molecular and crystal structures. You can install PyTorch Geometric using the following command.

>>> conda install pytorch-geometric -c rusty1s -c conda-forge
  1. Install required packages from requirements.txt in GitHub. After downloading the requirements file, you can install all required packages using the following commend.

>>> conda install --file requirements.txt
  1. (Optional) If your operating system is Windows, install Graphviz to visualize interpretable information of machine learning algorithms. You can install Graphviz using the following command.

>>> conda install -c conda-forge python-graphviz
  1. Finally, install MAICA in your Python environment with the following command.

>>> pip install maica

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