Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking
| Autor: | Orrette R. Wauchope, Ziqiao Xu, Aaron T. Frank |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
Computer science medicine.medical_treatment General Chemical Engineering Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Library and Information Sciences Antiviral Agents Molecular Docking Simulation chemistry.chemical_compound Molecular recognition Artificial Intelligence Panobinostat medicine Humans Protease Inhibitors Lidamidine Protease biology SARS-CoV-2 Drug discovery business.industry COVID-19 Active site General Chemistry Construct (python library) Chemical space Computer Science Applications Drug repositioning Drug development chemistry Docking (molecular) Interfacing biology.protein Artificial intelligence business Generative grammar |
| Zdroj: | Journal of Chemical Information and Modeling. 61:5589-5600 |
| ISSN: | 1549-960X 1549-9596 |
| Popis: | Here we report the testing and application of a simple, structure-aware framework to design target-specific screening libraries for drug development. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to rapidly explore chemical space conditioned on the unique physiochemical properties of the active site of a biomolecular target. As a proof-of-concept, we used our framework to construct a focused library for cyclin-dependent kinase type-2 (CDK2). We then used it to rapidly generate a library specific to the active site of the main protease (Mpro) of the SARS-CoV-2 virus, which causes COVID-19. By comparing approved and experimental drugs to compounds in our library, we also identified six drugs, namely, Naratriptan, Etryptamine, Panobinostat, Procainamide, Sertraline, and Lidamidine, as possible SARS-CoV-2 Mpro targeting compounds and, as such, potential drug repurposing candidates. To complement the open-science COVID-19 drug discovery initiatives, we make our SARS-CoV-2 Mpro library fully accessible to the research community (https://github.com/atfrank/SARS-CoV-2). |
| Databáze: | OpenAIRE |
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