TY - JOUR
T1 - PatentNetML
T2 - A Novel Framework for Predicting Key Compounds in Patents Using Network Science and Machine Learning
AU - Zhu, Ting-Fei
AU - Qian, Rong
AU - Wei, Xiao
AU - Lu, Ai-Ping
AU - Cao, Dong-Sheng
N1 - Publisher Copyright:
© 2024 American Chemical Society.
PY - 2024/1/25
Y1 - 2024/1/25
N2 - Patents play a crucial role in drug research and development, providing early access to unpublished data and offering unique insights.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.
AB - Patents play a crucial role in drug research and development, providing early access to unpublished data and offering unique insights.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.
UR - http://www.scopus.com/inward/record.url?scp=85182008838&partnerID=8YFLogxK
U2 - 10.1021/acs.jmedchem.3c01893
DO - 10.1021/acs.jmedchem.3c01893
M3 - Journal article
C2 - 38181431
AN - SCOPUS:85182008838
SN - 0022-2623
VL - 67
SP - 1347
EP - 1359
JO - Journal of Medicinal Chemistry
JF - Journal of Medicinal Chemistry
IS - 2
ER -