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Deep learning driven de novo drug design based on gastric proton pump structures.

Kazuhiro Abe1,2,3, Mami Ozako4, Miki Inukai4

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Researchers developed novel compounds targeting the gastric proton pump using AI-driven drug design and cryo-EM. The most potent compound, DQ-18, exhibits strong inhibitory potential for new drug development.

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Area of Science:

  • Medicinal Chemistry
  • Structural Biology
  • Computational Drug Discovery

Background:

  • Existing drugs face limitations like side effects and poor binding affinity.
  • Targeting the gastric proton pump is crucial for therapeutic development.

Purpose of the Study:

  • To design novel compounds with high inhibitory potency against the gastric proton pump.
  • To overcome limitations of existing drugs through structure-based de novo design.

Main Methods:

  • Utilized deep generative models for de novo drug design (Deep Quartet workflow).
  • Employed organic synthesis and cryo-electron microscopy (cryo-EM) for structural analysis.
  • Integrated in silico design with in vitro screening and structural validation.

Main Results:

  • Designed and synthesized candidate compounds based on drug-bound proton pump pharmacophores.
  • Identified DQ-18 (N-methyl-4-((2-(benzyloxy)-5-chlorobenzyl)oxy)benzylamine) as the most potent compound.
  • Achieved Ki value of 47.6 nM for DQ-18, with binding pose determined by 2.08 Å cryo-EM.

Conclusions:

  • The integrated approach enables structure-based de novo drug development.
  • High-resolution cryo-EM facilitates iterative compound design and optimization.
  • Demonstrated a successful framework for creating potent drug candidates targeting specific protein structures.