Predictive Pharmacokinetics Using Artificial Intelligence: A New Frontier in Rational Drug Discovery

Abstract

The integration of artificial intelligence and machine learning into pharmacokinetic prediction represents a fundamental advance in the quest to overcome the historical bottleneck of drug development. Current capabilities now extend beyond isolated property prediction, such as solubility or metabolic stability, toward sophisticated, integrated models that forecast comprehensive human PK profiles—including bioavailability, clearance, volume of distribution, and half-life—directly from molecular structure. These data-driven systems learn the complex, non-linear relationships that govern drug disposition, augmenting and often surpassing traditional metho ds like PBPK modeling, especially in early discovery where experimental data are sparse. By enabling de novo molecular design for optimal ADME properties and providing early, accurate human PK forecasts, AI/ML directly addresses the root cause of costly late-stage attrition, shifting the paradigm from reactive optimization to proactive, predictive design