A Prospective Virtual Screening Study: Enriching Hit Rates and Designing Focus Libraries To Find Inhibitors of PI3Kδ and PI3Kγ
| Autor: | Daniel DiSepio, Kelly L. Damm-Ganamet, Danielle Peeters, Scott D. Bembenek, Heather M. McAllister, James P. Edwards, Lieve Mangelschots, Taraneh Mirzadegan, Jennifer Venable, Glenda Castro |
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| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Prioritization Machine learning computer.software_genre Bioinformatics Molecular Docking Simulation 03 medical and health sciences Drug Discovery High-Throughput Screening Assays Prospective Studies Enzyme Inhibitors Phosphoinositide-3 Kinase Inhibitors Virtual screening Chemistry Drug discovery business.industry Isoenzymes 030104 developmental biology Docking (molecular) Drug Design Molecular Medicine Artificial intelligence business computer |
| Zdroj: | Journal of medicinal chemistry. 59(9) |
| ISSN: | 1520-4804 |
| Popis: | Here, we report a high-throughput virtual screening (HTVS) study using phosphoinositide 3-kinase (both PI3Kγ and PI3Kδ). Our initial HTVS results of the Janssen corporate database identified small focused libraries with hit rates at 50% inhibition showing a 50-fold increase over those from a HTS (high-throughput screen). Further, applying constraints based on "chemically intuitive" hydrogen bonds and/or positional requirements resulted in a substantial improvement in the hit rates (versus no constraints) and reduced docking time. While we find that docking scoring functions are not capable of providing a reliable relative ranking of a set of compounds, a prioritization of groups of compounds (e.g., low, medium, and high) does emerge, which allows for the chemistry efforts to be quickly focused on the most viable candidates. Thus, this illustrates that it is not always necessary to have a high correlation between a computational score and the experimental data to impact the drug discovery process. |
| Databáze: | OpenAIRE |
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