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Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.

Austin Clyde1,2

  • 1Department of Computer Science, University of Chicago, Chicago, IL, USA. aclyde@uchicago.edu.

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|November 3, 2021
PubMed
Summary
This summary is machine-generated.

Ultrahigh-throughput virtual screening (uHTVS) accelerates drug discovery by combining AI with docking. This study introduces image-based AI models for faster, large-scale molecular screening.

Keywords:
Chemical screeningDeep learningDrug discoveryGraph convolutionProtein–ligand dockingVirtual screening

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • cheminformatics

Background:

  • Classical docking methods are computationally intensive for large-scale screening.
  • Artificial intelligence (AI) offers potential for accelerating drug discovery workflows.
  • Ultrahigh-throughput virtual screening (uHTVS) integrates AI with traditional docking.

Purpose of the Study:

  • To present AI-accelerated workflows for uHTVS.
  • To introduce novel feature representation techniques for docking surrogate models.
  • To discuss the analysis and future of large-scale virtual screening.

Main Methods:

  • Utilizing AI-driven surrogate models for docking.
  • Employing molecular depictions (images) as a novel feature representation.
  • Developing regression enrichment surfaces for analyzing large-scale screens.

Main Results:

  • Demonstrated the efficacy of AI-accelerated workflows for uHTVS.
  • Showcased molecular images as effective surrogate models for docking.
  • Enabled analysis of screening data at the tens of billions scale.

Conclusions:

  • AI, particularly deep learning, is revolutionizing uHTVS.
  • Image-based representations offer a promising avenue for surrogate docking models.
  • Future uHTVS pipelines will increasingly leverage advanced AI techniques for drug discovery.