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

Journal: Journal of chemical information and modeling
Published Date: Jan 6, 2026

Abstract

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 (VDss) 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.

Authors

  • Jiaojiao Fang
    Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
  • Changda Gong
    Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
  • Keyun Zhu
    Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Chen Yang
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Zhixing Zhang
    School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
  • Guixia Liu
    Shanghai Key Laboratory of New Drug Design , School of Pharmacy , East China University of Science and Technology , Shanghai 200237 , China . Email: [email protected] ; Email: [email protected] ; ; Tel: +86-21-64250811.
  • Yun Tang
    Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Weihua Li
    State Key Laboratory of Molecular Engineering of Polymers, Key Laboratory of Computational Physical Sciences, Department of Macromolecular Science, Fudan University, Shanghai 200438, China.

Keywords

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