Evidence map›Paper›PMID 42595948›Full record

ArticleTherapeutic innovation & regulatory science2026

Exploring the Polyethylene Glycol-Modified Drug Patent Landscape by Deep Learning.

Tingting Zhang, Dechao Deng, Xiaoming Zhang, Weijie Chen, Pingping Wang, Xiang Li, Jinyu Cong, Benzheng Wei, Kunmeng Liu

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Article in Therapeutic innovation & regulatory science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tingting Zhang *Center for Medical Artificial Intelligence, Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, Qingdao, China.
Dechao Deng *Center for Medical Artificial Intelligence, Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, Qingdao, China.
Xiaoming Zhang *Department of Cardiovascular Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Weijie ChenState Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau, 999078, China.
Pingping WangCenter for Medical Artificial Intelligence, Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, Qingdao, China.
Xiang LiCenter for Medical Artificial Intelligence, Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, Qingdao, China.
Jinyu CongCenter for Medical Artificial Intelligence, Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, Qingdao, China.
Benzheng WeiCenter for Medical Artificial Intelligence, Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, Qingdao, China.
Kunmeng LiuCenter for Medical Artificial Intelligence, Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, Qingdao, 266112, Qingdao, China. liukunmeng@sdutcm.edu.cn.

Funding

National Natural Science Foundation of China 62372280Natural Science Foundation of Shandong Province ZR2026MS0989Scientific Research Fund Project of Shandong University of Chinese Medicine KYRW2024Q04
6 · The paper itself

Abstract

objectiveRecently, polyethylene glycol modification has become key to improving biopharmaceutical pharmacokinetics and clinical applicability. This study aims to build a comprehensive analytical framework that integrates current status analysis, technology flow, and value assessment, in order to provide a step-by-step and thorough characterization of the patent landscape for PEG-modified drugs.

methodsUsing the Derwent patent database, this study compiled 99,540 PEG-related patents worldwide from 2014 to 2023. Descriptive statistics, social network analysis, machine learning, and deep learning methods were applied to analyze these patents.

resultsThe number of related patents increased dramatically over the past decade. China filed the most patents (24303) but exhibited a narrower technological breadth, while the United States led in numbers of inventors (86208) and assignees (37045). Patents from developed regions are more likely to be cited, and patent transfer activities mainly occur between commercial institutions. PEG-modified proteins and peptides represent the most commercially active category, highlighting their current market relevance. For patent transfer prediction, the XGBoost model achieved an average accuracy of 88.15%, an average F1-score of 88.28%, and a test ROC-AUC of 87.00%. For early-stage patent quality assessment, the RoBERTa-BiLSTM-MLP model achieved an accuracy of 65.95% and an F1-score of 64.53%.

conclusionThese analyses provide a multi-layered understanding of the PEG-modified drug patent landscape, from static features to dynamic trends and from quantitative indicators to qualitative evaluations. These findings provide an analytical reference for exploring technology trends in the field of PEG-modified drugs.

Indexed as

Deep learningNetwork analysisPatent qualityPatent reviewPatent transferPolyethylene glycol

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.