Deep Genomics combines artificial intelligence (AI) and RNA biology to program and prioritize transformational AI-enabled therapies for almost any gene in any genetic condition. Our proprietary platform, called the AI Workbench, allows Deep Genomics to decode vast amounts of data on RNA biology, identify novel targets for genetic diseases, and produce therapeutic programs with a high success rate. In this talk, I'll outline our end-to-end drug development process with AI at its core, and give examples of some recent breakthroughs that have allowed us to make accurate predictions of variant effects and rapid identification of the active and potent therapeutic compounds.
Shreshth Gandhi leads the Machine Learning group at Deep Genomics, a biotechnology company that uses ML to program and prioritize transformational RNA therapeutics for genetic diseases. He received his master's degree from the University of Toronto, where his research work focused on developing deep learning predictors for predicting RNA-protein binding. At Deep Genomics he continued this work at the intersection of deep learning and genomics and co-developed the splicing predictor that was used to identify that the ATP7B Variant c.1934T>G p.Met645Arg causes Wilson Disease by altering splicing.