Machine Learning Meets Pharmacokinetics: A Comparative Analysis of Predictive Models for Plasma Concentration-Time Profiles.

Predicting pharmacokinetic (PK) profiles from molecular structures represents a significant advancement in pharmaceutical research with substantial implications for expediting drug discovery processes. We evaluated five approaches to systematically compare five distinct methodological frameworks for predicting rat plasma concentration-time profiles directly from molecular structures using a consistent dataset and evaluation framework: (1) NCA-ML, predicted non-compartmental analysis parameters with one compartmental PK modeling; (2) PBPK-ML, utilizing ML-predicted in vitro characteristics in physiologically based PK models; (3) CMT-ML, neural networks predicting compartmental PK model parameters with two or three compartmental PK modeling; (4) CMT-PINN, employing physics-informed neural networks trained on concentration-time profiles predicting compartmental PK model parameters with two or three compartmental PK modeling; and (5) PURE-ML, using decision trees to predict concentration values at specific time points. The CMT-PINN approach achieved highest predictive performance closely followed by PURE-ML (R