PatentNetML: A Novel Framework for Predicting Key Compounds in Patents Using Network Science and Machine Learning-Reference-Cited by-同舟云学术

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

Published:2024-01-05 Issue:2 Volume:67 Page:1347-1359
ISSN:0022-2623
Container-title:Journal of Medicinal Chemistry
language:en
Short-container-title:J. Med. Chem.

Author:

Zhu Ting-Fei¹²,Qian Rong¹²,Wei Xiao¹,Lu Ai-Ping²³,Cao Dong-Sheng¹²^ORCID

Affiliation:

1. Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, Hunan, China

2. School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR 999077, China

3. Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou 510000, China

Funder

Natural Science Foundation of Hunan Province

National Key Research and Development Program of China

Science and Technology Program of Hunan Province

Science Fund for Distinguished Young Scholars of Hunan Province

Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jmedchem.3c01893

Reference40 articles.

1. Patent searches as a complement to literature searches in the life sciences—a 'how-to' tutorial

2. Expanding opportunities for mining bioactive chemistry from patents

3. Assessment of the significance of patent-derived information for the early identification of compound–target interaction hypotheses

4. Identification of the Core Chemical Structure in SureChEMBL Patents

5. Text mining techniques for patent analysis

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