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

OBJECTIVE: Recently, 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. METHODS: Using 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. RESULTS: The 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%. CONCLUSION: These 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.