Author: Xu, Ziqiao; Wauchope, Orrette R; Frank, Aaron T
Title: Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking Cord-id: lwpvuh25 Document date: 2021_1_1
ID: lwpvuh25
Snippet: 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-depe
Document: 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.
Search related documents:
Co phrase search for related documents- Try single phrases listed below for: 1
Co phrase search for related documents, hyperlinks ordered by date