LSTM-Based Prediction of Human PK Profiles and Parameters for Intravenous Small Molecule Drugs Using ADME and Physicochemical Properties.

ABSTRACT: Accurate prediction of human pharmacokinetics (PK) for lead compounds is one of the critical determinants of successful drug development. Traditional methods for PK parameter prediction, such as in vitro to in vivo extrapolation and physiologically based pharmacokinetic modeling, often require extensive experimental data and time-consuming calibration of parameters. Machine learning (ML) has been widely applied to predict ADME and physicochemical properties (ADMEP descriptors), but studies focusing on concentration-time (C-t) profile prediction remain limited. In this study, we developed a Long Short-Term Memory (LSTM) based ML framework to predict C-t profiles following intravenous (IV) bolus drug administration in humans. The model used ADMEP descriptors generated by ADMETlab 3.0 and dos

SUBMITTER: Luo P 

PROVIDER: S-EPMC12706403 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature