文献类型：期刊文献

英文题名：Prediction of Intravenous Pharmacokinetic Parameters across Multiple Species by a Multifidelity Deep Learning Framework

作者：Fang, Jiaojiao[1];Gong, Changda[1];Zhu, Keyun[1];Li, Xiang[1];Yang, Chen[1];Zhang, Zhixing[1];Liu, Guixia[1];Tang, Yun[1];Li, Weihua[1]

机构：[1]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China

年份：2026

卷号：66

期号：2

起止页码：1035

外文期刊名：JOURNAL OF CHEMICAL INFORMATION AND MODELING

收录：;EI(收录号：20260419965455);WOS:【SCI-EXPANDED(收录号:WOS:001655279900001)】；

基金：This work was supported by the National Natural Science Foundation of China (grants 82373797 and U23A20530) and the National Key Research and Development Program of China (grant 2023YFF1204904).

语种：英文

外文关键词：Decision making - Deep learning - Forecasting - Learning systems - Mammals - Optimization - Screening - Transfer learning

摘要：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.