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Computational approaches are now sufficiently efficient and accurate to have significant impact on diverse facets of structure-based drug discovery campaigns. This webcast will illustrate how automated large-scale enumerations, machine learning approaches and physics-based profiling can accelerate early-stage drug discovery efforts.
The presentation will describe a synthetically-aware enumeration strategy that generates design ideas which possess project-specific physicochemical properties and which efficiently and systematically explore new IP space as well as distinct regions of the targeted binding pocket. It will also highlight best practices for active learning approaches to free energy calculations and how free energy predictions are being used at scale by drug discovery teams to accelerate project timelines.
You will learn: