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Comprehensive Exploration of Target-specific Ligands Using a Graph Convolution Neural Network.

Yu Miyazaki1, Naoaki Ono1,2, Ming Huang1

  • 1Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.

Molecular Informatics
|December 10, 2019
PubMed
Summary

This study introduces a novel machine learning method to identify selective drug ligands without negative data. The approach effectively distinguishes target ligands, aiding in the development of safer therapeutics for diseases like Alzheimer's.

Keywords:
BACE1GCNNcathepsin Dligand selectivitymapping of principal components

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Area of Science:

  • Computational chemistry and cheminformatics
  • Machine learning in drug discovery
  • Bioinformatics and computational biology

Background:

  • Machine learning models are crucial for evaluating ligand-protein interactions in drug development.
  • A key challenge is the scarcity of negative data (compounds that do not bind to a target protein).
  • Developing highly selective ligands is vital for enhancing drug safety by minimizing off-target effects.

Purpose of the Study:

  • To propose a novel machine learning approach for identifying selective ligand candidates without relying on negative interaction data.
  • To develop a method that distinguishes ligands for a target protein from those causing off-target effects.
  • To apply this method for exploring potential drug candidates for Alzheimer's Disease (AD) targeting BACE1 with minimal cathepsin D off-target activity.

Main Methods:

  • Utilized a graph convolution neural network (GCNN) to build a classifier distinguishing target from off-target ligands.
  • Extracted feature vectors, reduced dimensionality using principal component analysis (PCA), and visualized in 2D.
  • Employed kernel density estimation (KDE) to define high-density regions for ligand groups and mapped exploration compounds.

Main Results:

  • The GCNN model successfully differentiated ligands for the target protein (BACE1) from off-target proteins (cathepsin D).
  • Ligand feature vectors mapped onto a 2D density plot showed distinct regions for BACE1 and cathepsin D binders.
  • Exploration compounds, including natural compounds, exhibited significantly different distributions on the density map, indicating potential as drug candidates.

Conclusions:

  • The proposed method effectively identifies selective ligand candidates without requiring negative interaction data.
  • The approach facilitates the exploration of compounds with high affinity for the target protein and low affinity for off-target proteins.
  • This strategy holds promise for accelerating the discovery of safer and more effective drug candidates, particularly for complex diseases like Alzheimer's.