Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic (PK) properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic (PBPK) simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with advanced generative chemistry algorithms. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine (TzP) inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum (PfDHODH) to illustrate how AIDD generates novel sets of molecules.

By Robert D. Clark, Jeremy Jones, Michael S. Lawless, David W. Miller, Marvin Waldman