Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking.
Ziqiao XuOrrette R WauchopeAaron T FrankPublished in: Journal of chemical information and modeling (2021)
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.
Keyphrases
- artificial intelligence
- molecular docking
- machine learning
- big data
- sars cov
- deep learning
- molecular dynamics simulations
- cell cycle
- primary care
- healthcare
- tyrosine kinase
- cell proliferation
- cell death
- health information
- quality improvement
- respiratory syndrome coronavirus
- signaling pathway
- single molecule
- protein kinase
- cell cycle arrest