Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking

J Chem Inf Model. 2021 Nov 22;61(11):5589-5600. doi: 10.1021/acs.jcim.1c00746. Epub 2021 Oct 11.


Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Antiviral Agents
  • Artificial Intelligence*
  • COVID-19*
  • Humans
  • Molecular Docking Simulation
  • Protease Inhibitors
  • SARS-CoV-2


  • Antiviral Agents
  • Protease Inhibitors