Pharmacometrics-Informed Deep Learning with DeepNLME Chris Rackauckas and Vijay Ivaturi Department of Mathematics, Massachusetts Institute of Technology Pumas-AI, United States School of Pharmacy, University of Maryland, Baltimore Nonlinear mixed effects modeling (NLME) is commonly employed throughout the clinical pharmacometrics community in order to uncover covariate relationships and understand the personalized effects involved in drug kinetics. However, in many cases a full model of drug dynamics is unknown. Even further, common models used throughout clinical trials ignore many potentially predictive covariates as their connection to drug effects is unknown. Given the rise of machine learning, there have been calls to utilize deep learning techniques to potentially uncover these unknown relationships, but common deep learning techniques are unable to incorporate the prior information captured in known predictive models and thus are not predictive with the minimal data available. Thus, the question: is it possible to bridge the gap between deep learning and nonlinear mixed effects modeling? In this talk we will describe the DeepNLME method for performing automatic discovery of dynamical models in NLME along with discovery of covariate relationships. We will showcase how this extension of the universal differential equation framework is able to generate suggested models in a way that hypothesizes testable mechanisms, predicts the covariates of interest, and allows incorporating data in the form of images and sequences into the personalized precision dosing framework. This framework and the automated model discovery process will be showcased in the Pumas pharmaceutical modeling and simulation environment. We will end by describing how this is being combined with recent techniques from Bayesian Neural Ordinary Differential Equations in order to give probabilistic estimates to the discovered models and allow for direct uncertainty quantification. Together this demonstrates a viable path for incorporating all of the knowledge of pharmacometricians into the data-driven future. This work uses the Julia Programming Language.