MMPK: A Multimodal Deep Learning Framework to Predict Human Oral Pharmacokinetic Parameters

Accurate prediction of in vivo pharmacokinetic
(PK) profiles is crucial for assessing drug safety and efficacy, optimizing
dosage regimens, and understanding interactions between the human
body and drugs. Using machine learning to predict PK parameters has
the potential to considerably save time and resources during drug
development. In this study, we constructed a human oral PK data set
containing over 1,200 unique compounds and more than 5,000 compound–dose
combinations. Building on this data set, we developed a multimodal
deep learning framework named MMPK, integrating molecular graphs,
substructure graphs, and SMILES sequences to capture multiscale molecular
information. MMPK employs multitask learning and data imputation to
improve data efficiency and model robustness. Comparative evaluations
confirm that MMPK outperforms baseline models, achieving an average
geometric mean fold error (GMFE) of 2.895 and root mean squared logarithmic
error (RMSLE) of 0.599 across eight PK parameters. The MMPK model
is freely accessible at https://lmmd.ecust.edu.cn/mmpk/.