ArticleSupercomputing frontiers and innovations2020
Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method.
Article in Supercomputing frontiers and innovations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identification of putative allosteric inhibitors of BCKDK via virtual screening and biological evaluation.Journal of enzyme inhibition and medicinal chemistry · 2024Article
- Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.Current opinion in chemical biology · 2022Review
- VirtualFlow Ants-Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization.International journal of molecular sciences · 2021Article
- A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening.iScience · 2021Article
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4 authors.
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Abstract
Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role.
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