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DeepRT: Predicting compounds presence in pathway modules and classifying into module classes using deep neural

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A new deep learning model, DeepRT, accurately predicts compound-pathway module relationships. This advances understanding of metabolic pathways and aids in synthesizing new molecules and discovering reactions.

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Systems Biology

Background:

  • Metabolic pathways are fundamental to organismal biochemistry.
  • Pathway modules, clusters of reactions, contain key compounds (substrates, intermediates, products).
  • Identifying compound-pathway module relationships is vital for molecular synthesis and reaction prediction.

Purpose of the Study:

  • To develop a novel computational method for predicting compound-pathway module relationships.
  • To address limitations of existing methods that do not predict pathway modules.

Main Methods:

  • Proposed a deep learning model, DeepRT.
  • Integrated message passing neural networks (MPNNs) and a transformer encoder.
  • Utilized molecular graph structure for information extraction.

Main Results:

  • DeepRT effectively extracts global and local structural information.
  • The model performs two tasks: predicting compound presence in pathway modules and classifying query compound-module relationships.
  • DeepRT outperforms existing methods on a dedicated dataset.

Conclusions:

  • DeepRT offers a significant advancement in predicting compound-pathway module interactions.
  • The model enhances the prediction of metabolic pathway components.
  • This facilitates novel molecule synthesis and the discovery of previously unknown biochemical reactions.