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DeepSIBA: chemical structure-based inference of biological alterations using deep learning.

C Fotis1, N Meimetis, A Sardis

  • 1Biomedical Systems Laboratory, National Technical University of Athens, Athens, Greece. leo@mail.ntua.gr.

Molecular Omics
|November 14, 2020
PubMed
Summary

This study introduces a deep learning model for predicting drug effects from chemical structures. The model accurately identifies compounds affecting similar biological processes and predicts pathways influenced by specific molecular substructures.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Pharmacology

Background:

  • Predicting biological effects of chemical structures is crucial for in silico drug discovery.
  • Current methods face challenges in accurately linking molecular structures to biological outcomes.

Purpose of the Study:

  • To develop a deep learning model for predicting biological effects from chemical structures.
  • To identify structurally dissimilar compounds with similar biological impacts.
  • To infer signaling pathway signatures and influential substructures from chemical structures.

Main Methods:

  • Utilized Siamese Graph Convolutional Neural Networks to map compound differences to biological effect alterations.
  • Represented chemical structures as graphs for deep learning analysis.

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  • Employed deep ensembles for uncertainty estimation in predictions.

    • Main Results:

    • The model accurately encoded molecular graph pairs and identified structurally dissimilar compounds affecting similar biological processes.
    • Achieved high precision in predicting biological effects.
    • Provided reliable predictions for novel chemical structures outside the training set.
    • Successfully inferred signaling pathway signatures and influential substructures for anticancer drugs.

    Conclusions:

    • The developed deep learning model offers a powerful tool for in silico drug discovery.
    • The approach enables accurate prediction of biological effects and identification of key molecular drivers.
    • This method has significant potential for accelerating drug development and understanding drug mechanisms.