An Artificial Intelligence Framework for Optimal Drug Design
Santiago Vargas, University of California, Los Angeles
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.
Abstract Author(s): Grace Ramey, Santiago Vargas, Dinesh De Alwis, Anastassia Alexandrova, Joe Distefano III, Peter Bloomingdale