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Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking.

Ziqiao Xu1, Orrette R Wauchope2, Aaron T Frank3

  • 1Chemistry Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States.

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We developed a novel framework combining AI and molecular docking to create targeted compound libraries. This method explores chemical space based on a target

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Structural biology

Background:

  • Generating targeted screening libraries is crucial for efficient drug discovery.
  • Exploring vast chemical spaces requires advanced computational methods.
  • Understanding biomolecular target active sites is key to designing specific inhibitors.

Purpose of the Study:

  • To implement and apply a simple, structure-aware framework for generating target-specific screening libraries.
  • To combine generative artificial intelligence (AI) with molecular docking.
  • To demonstrate the framework's utility by creating libraries for CDK2 and SARS-CoV-2 Mpro.

Main Methods:

  • A structure-aware framework integrating generative AI and molecular docking.
  • Chemical space exploration conditioned on target active site physicochemical properties.
  • Application of the 'sample-and-dock' framework to cyclin-dependent kinase type-2 (CDK2) and SARS-CoV-2 main protease (Mpro).

Main Results:

  • Successful implementation of the 'sample-and-dock' framework.
  • Generation of focused screening libraries for specific biomolecular targets.
  • Demonstration of the framework's capability to tailor libraries to target active site characteristics.

Conclusions:

  • The 'sample-and-dock' framework offers a novel approach to generating target-specific screening libraries.
  • This AI-driven method can map chemical space relevant to a target's molecular recognition.
  • The framework has potential applications in accelerating drug discovery and understanding molecular interactions.