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kGCN: a graph-based deep learning framework for chemical structures.

Ryosuke Kojima1, Shoichi Ishida2, Masateru Ohta3

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This summary is machine-generated.

An open-source tool, kGCN, democratizes graph convolutional neural networks (GCNs) for cheminformatics. It offers user-friendly interfaces and explainable AI features for reliable molecular predictions.

Keywords:
Graph convolutional networkGraph neural networkKNIMEOpen source softwarekGCN

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Area of Science:

  • Cheminformatics
  • Artificial Intelligence
  • Computational Chemistry

Background:

  • Deep learning, particularly graph convolutional neural networks (GCNs), shows promise for molecular prediction tasks in cheminformatics.
  • Effective utilization of GCNs requires specialized knowledge of the algorithms and programming skills.
  • Accessibility to GCNs is limited for chemists and cheminformaticians with varying technical expertise.

Purpose of the Study:

  • To introduce kGCN, an open-source graph convolutional neural network tool designed to make GCNs more accessible for cheminformatics applications.
  • To provide multiple interfaces catering to users with different programming skill levels, including chemists and cheminformaticians.
  • To facilitate the entire process of building predictive models, from data pre-processing to model interpretation.

Main Methods:

  • Developed kGCN with three interfaces: a graphical user interface (GUI) via KNIME, a command-line interface, and a Python library.
  • Integrated functions for data pre-processing, Bayesian optimization for automated model tuning, and visualization of atomic contributions for result interpretation.
  • Supported single-task, multi-task, and multi-modal prediction approaches.
  • Conducted a case study predicting compound-protein interactions for matrix-metalloproteases (MMP-3, -9, -12, -13).

Main Results:

  • kGCN successfully predicted compound-protein interactions for the selected MMPs.
  • The tool provides visualization of atomic contributions, enabling explainable AI for understanding prediction drivers.
  • The case study demonstrated the utility of kGCN for model validation and molecular design.

Conclusions:

  • kGCN enhances the usability of GCNs in cheminformatics by offering flexible interfaces and comprehensive functionalities.
  • The tool supports explainable AI, aiding in the validation and interpretation of molecular prediction models.
  • kGCN empowers a wider range of users, from chemists to cheminformaticians, to leverage GCNs for molecular research.