Prediction of Intravenous Pharmacokinetic Parameters across Multiple Species by a Multifidelity Deep Learning Framework

Prediction of pharmacokinetic (PK) properties is essential for early drug candidate screening and dosage regimen optimization. In recent years, using machine learning/deep learning approaches for predicting pharmacokinetic properties directly from chemical structures has attracted increasing attention. In this study, we propose multifidelity pharmacokinetic learning (MFPK), a transfer-learning framework for predicting intravenous pharmacokinetic parameters across multiple species, including humans, dogs, monkeys, rats, and mice. MFPK incorporates graph-, motif-, and three-dimensional structure-based molecular representations to capture comprehensive, multiscale chemical information. Comparative evaluations demonstrate that MFPK outperforms baseline models across multiple tasks, particularly volume of distribution at steady state (VD_ss) across all species (root-mean-square of logarithmic error (RMSLE) < 0.48, geometric mean fold error (GMFE) < 2.3). Furthermore, interpretability analyses were conducted to provide insights into model decision-making and mitigate the black-box nature of deep learning models. The MFPK model is accessible at https://lmmd.ecust.edu.cn/MFPK.