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CIGIN : Chemically Interpretable Graph Interaciton Network

Official implementation of CIGIN presented at proceedings of the 34th AAAI conference on Artificial Intelligence, AAAI-20.

CIGIN is a chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like molecules.

Requirements:

• PyTorch
• Numpy
• RDKit

Usage:

• Examples for prediction and analysis of interaction between solute and solvent atoms are given in the notebook.
• Required scripts are given in the scripts folder.
• Trained model weight is provided in weights folder.

People

• Yashaswi Pathak
• Siddhartha Laghuvarapu
• Sarvesh Mehta
• U. Deva Priyakumar

If you find this useful in your research, please cite:

@inproceedings{pathak2020chemically,
title={Chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like 		  molecules},
  author={Pathak, Yashaswi and Laghuvarapu, Siddhartha and Mehta, Sarvesh and Priyakumar, U Deva},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={01},
  pages={873--880},
  year={2020}
}