Abstract
PROteolysis TArgeting Chimeras (PROTACs) are bifunctional molecules that offer a novel approach to targeted protein degradation, showing particular promise for previously ‘undruggable’ targets. Despite their therapeutic potential, significant challenges remain in optimizing the pharmacokinetic (PK) properties of PROTACs, particularly in terms of their ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics. In this study, we drew on the experience of previous work and combined traditional machine learning with multiple molecular fingerprints to propose a PROTAC pharmacokinetic property prediction model EGFR-PROPK. We conducted in-vivo experiments on 100 EGFR-targeting PROTAC molecules, focusing on clearance (CL), half-life (T1/2), and apparent volume of distribution (Vss), which are the most intuitive and commonly used macro parameters to describe the PK properties of a drug since they describe the absorption-distribution-elimination process of drugs in the body from different perspectives. Our findings reveal that traditional models trained on small molecules perform poorly on PROTACs. However, training the models on PROTAC-specific data significantly improved prediction accuracy, achieving correlation coefficients of 0.78, 0.75, and 0.52 between the predicted and observed values for T1/2, CL and Vss, respectively, highlighting the need for tailored approaches in PK evaluation for these unique molecules. These insights are critical for advancing the design and development of PROTAC-based therapies.
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Data availability
The ADMET dataset ultilized for pre-training is sourced from the public accessible Therapeutics Data Commons (TDC), avaliable at https://tdcommons.ai/.
Code availability
All the data sets and source code are publicly available through the GitHub(https://github.com/Zhang-Ran-0119/EGFR-PROPK). The custom code used in this study is available at GitHub and has been archived on Zenodo with a persistent https://doi.org/10.5281/zenodo.18276159. The repository includes all scripts required to reproduce the reported results.
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Acknowledgements
This work was supported by National Key R&D Program of China (to F. B., Grant IDs: 2022YFC3400501 and 2022YFC3400500), Shanghai Science and Technology Development Fund (to F. B., 22ZR1441400), Shanghai Pujiang Program (to Y. L., Grant No. 23PJ1420700), ShanghaiTech AI4S Initiative SHTAI4S202404, start-up package from ShanghaiTech University, and Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at ShanghaiTech University. We are grateful for the support from HPC Platform of ShanghaiTech University.
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R.Z. and F.L. constructed and trained the network model and wrote the manuscript; Q.Z., Y.L., Y.G., and L.Z. conducted the experiments and provide data; F.B. and L.Z. designed the whole project and revised the manuscript.
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The authors have no relevant financial or non-financial interests to disclose. Fang Bai is an Editorial Board Member for Communications Chemistry, but was not involved in the editorial review of, or the decision to publish this article.
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Zhang, R., Li, F., Liu, Y. et al. A machine learning-based pharmacokinetics predictor (EGFR-PROPK) for EGFR-targeting PROTACs. Commun Chem (2026). https://doi.org/10.1038/s42004-026-01938-3
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DOI: https://doi.org/10.1038/s42004-026-01938-3
