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PatentNetML: A Novel Framework for Predicting Key Compounds in Patents Using Network Science and Machine Learning.
Zhu, Ting-Fei; Qian, Rong; Wei, Xiao; Lu, Ai-Ping; Cao, Dong-Sheng.
Afiliação
  • Zhu TF; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, Hunan, China.
  • Qian R; School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, China.
  • Wei X; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, Hunan, China.
  • Lu AP; School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, China.
  • Cao DS; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, Hunan, China.
J Med Chem ; 67(2): 1347-1359, 2024 01 25.
Article em En | MEDLINE | ID: mdl-38181431
ABSTRACT
Patents play a crucial role in drug research and development, providing early access to unpublished data and offering unique insights.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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Med Chem Assunto da revista: QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Med Chem Assunto da revista: QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos