<scp>LSTM</scp> ‐Based Prediction of Human <scp>PK</scp> Profiles and Parameters for Intravenous Small Molecule Drugs Using <scp>ADME</scp> and Physicochemical Properties

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 dose information as input. A total of 40 drugs were used for training and 18 for testing, with concentration data simulated from published PK models. Our approach achieved R² of 0.75 across all C-t profiles, and 77.8% of C_max, 55.6% of clearance, and 61.1% of volume of distribution predictions within a 2-fold error range, demonstrating predictive performance comparable to previously published ML methods. Furthermore, model performance was found to be associated with the input dose level and ADMEP descriptors, suggesting the accuracy and confidence of the prediction may be expected in advance via these descriptors. This LSTM-based framework using a small number of compounds enables efficient prediction of human PK profiles with IV dosing, offering a practical alternative to traditional PK prediction models. It holds promise for improving early-phase prioritizing lead compounds and reducing reliance on animals in drug development.