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/.