PatentNetML: A Novel Framework for Predicting Key Compounds in Patents Using Network Science and Machine Learning

Ting-Fei Zhu, Rong Qian, Xiao Wei, Ai-Ping Lu*, Dong-Sheng Cao*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Patents play a crucial role in drug research and development, providing early access to unpublished data and offering unique insights. Identifying key compounds in patents is essential to finding novel lead compounds. This study collected a comprehensive data set comprising 1555 patents, encompassing 1000 key compounds, to explore innovative approaches for predicting these key compounds. Our novel PatentNetML framework integrated network science and machine learning algorithms, combining network measures, ADMET properties, and physicochemical properties, to construct robust classification models to identify key compounds. Through a model interpretation and an analysis of three compelling case studies, we showcase the potential of PatentNetML in unveiling hidden patterns and connections within diverse patents. While our framework is pioneering, we acknowledge its limitations when applied to patents that deviate from the assumed central pattern. This work serves as a promising foundation for future research endeavors aimed at efficiently identifying promising drug candidates and expediting drug discovery in the pharmaceutical industry.

Original languageEnglish
Pages (from-to)1347–1359
Number of pages13
JournalJournal of Medicinal Chemistry
Issue number2
Early online date5 Jan 2024
Publication statusPublished - 25 Jan 2024

Scopus Subject Areas

  • Molecular Medicine
  • Drug Discovery


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