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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Convolution computations can be simplified by utilizing their inherent properties.
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Graph convolutional networks for computational drug development and discovery.

Mengying Sun1, Sendong Zhao2, Coryandar Gilvary3

  • 1Department of Computer Science and Engineering, Michigan State University, East Lansing, MI USA.

Briefings in Bioinformatics
|June 4, 2019
PubMed
Summary
This summary is machine-generated.

Graph convolutional networks (GCNs) are revolutionizing molecular informatics and drug discovery by enabling deep learning on complex chemical structures. This review explores their applications from property prediction to de novo drug design.

Keywords:
computational drug developmentgraph convolution network

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Artificial intelligence in drug discovery

Background:

  • Deep learning shows promise but has limited application in molecular informatics.
  • Recent advances allow deep architectures to process structured data, opening new avenues in pharmaceutical research.

Purpose of the Study:

  • To systematically review graph convolutional networks (GCNs) and their applications in drug discovery and molecular informatics.
  • To explain how GCNs contribute to various drug-related tasks.

Main Methods:

  • Introduction to the theoretical foundations of GCNs.
  • Illustration of various GCN architectures.
  • Summarization of GCN applications in drug discovery.

Main Results:

  • GCNs are applied to molecular property and activity prediction.
  • GCNs are used for interaction prediction, synthesis prediction, and de novo drug design.
  • Representative applications of GCNs in drug-related problems are summarized.

Conclusions:

  • GCNs offer a powerful new paradigm for molecular informatics and drug discovery.
  • Current challenges and future possibilities for GCNs in drug discovery are discussed.