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

ABSTRACT 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 2 ‐log: 0.854 vs. 0.789, Spearman: 0.933 vs. 0.896), with 65.9% versus 61.0% of predictions within a twofold error of the observed concentrations. The other three approaches showed substantially lower performance metrices and higher prediction error margins. Models trained directly on concentration‐time profiles outperformed those trained using derived PK parameters, particularly with limited training datasets. Our findings confirm the viability of predicting PK behavior from molecular structures prior to synthesis. The implementation of these computational approaches enables informed compound selection early in discovery, concentrating resources on promising candidates, and potentially reducing animal studies while accelerating development timelines.