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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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PathwayMap: Molecular Pathway Association with Self-Normalizing Neural Networks.

José Jiménez1, Davide Sabbadin1, Alberto Cuzzolin2

  • 1Computational Science Laboratory , Universitat Pompeu Fabra , Barcelona Biomedical Research Park (PRBB), Carrer del Dr. Aiguader 88 , 08003 , Barcelona , Spain.

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This study introduces a deep neural network to predict molecular pathway associations, improving drug discovery by identifying potential issues early. The model enhances the understanding of compound activity profiles, reducing attrition in drug development.

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Bioinformatics

Background:

  • High attrition rates in drug discovery are often due to a lack of understanding of compound activity profiles.
  • Predicting molecular pathway associations is crucial for identifying potential ineffectiveness or toxicity early in the drug discovery process.

Purpose of the Study:

  • To develop and evaluate a deep self-normalizing neural network model for predicting molecular pathway association.
  • To assess the model's performance on diverse datasets and discuss its utility in lead discovery.

Main Methods:

  • Development of a deep self-normalizing neural network architecture.
  • Training and validation of the model using compound data from ChEMBL.
  • External validation using a dataset provided by Novartis.

Main Results:

  • The model achieved an Area Under the Curve (AUC) ranging from 0.69 to 0.91 on ChEMBL data.
  • The model demonstrated strong performance on an external Novartis dataset with AUCs from 0.81 to 0.83.
  • The model shows promise for predicting molecular pathway associations.

Conclusions:

  • The proposed deep neural network model is effective in predicting molecular pathway associations.
  • This approach can aid in reducing attrition rates in drug discovery by providing a better understanding of compound activity.
  • The model has practical applications in the lead discovery phase of drug development.