The prevalence of ionizable compounds in pharmaceuticals makes pKa an important physicochemical property to consider in drug discovery and development. In this presentation, Andrew Anderson highlights the need for accurate predictive models, particularly with the ever-increasing interest in incorporating generative AI models in pharmaceutical R&D and digital twin simulation of physical entities. Walk through the data-rich pKa prediction interfaces delivered by Percepta and see how you can leverage distributed computing via Percepta portal for generative AI at scale.