An Artificial Intelligence Framework for Optimal Drug Design

We introduce the concept of optimal drug design (ODD) as the use of an AI framework to optimize the
exposure, safety, and efficacy of drugs. To exemplify the concept of ODD, we developed an artificial
intelligence framework that integrates de novo molecular design, quantitative structure activity
relationships, and pharmacokinetic-pharmacodynamic modeling. Specifically, our computational
architecture has integrated a generative algorithm for small molecule design with a hybrid
physiologically-based pharmacokinetic machine learning (PBPK-ML) model, which was applied to
generate and optimize drug candidates for enhanced brain exposure. Publicly sourced data on the plasma
and brain pharmacokinetics of 77 small molecule drugs in rats was used for model development. We have
observed an approximate 30-fold and 120-fold increase on average in predicted brain exposure for AI
generated molecules compared to known central nervous system drugs and randomly selected small
organic molecules. We believe that with additional data and model refinement this in silico pipeline could
facilitate the discovery of a new wave of optimally designed medicines for the treatment of CNS diseases.