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NPCDR: natural product-based drug combination and its disease-specific molecular regulation.

Xueni SunYintao ZhangYing ZhouXichen LianLili YanTing PanTing JinHan XieZimao LiangWenqi QiuJianxin WangZhaorong LiJian ZhangXinbing Sui
Published in: Nucleic acids research (2021)
Natural product (NP) has a long history in promoting modern drug discovery, which has derived or inspired a large number of currently prescribed drugs. Recently, the NPs have emerged as the ideal candidates to combine with other therapeutic strategies to deal with the persistent challenge of conventional therapy, and the molecular regulation mechanism underlying these combinations is crucial for the related communities. Thus, it is urgently demanded to comprehensively provide the disease-specific molecular regulation data for various NP-based drug combinations. However, no database has been developed yet to describe such valuable information. In this study, a newly developed database entitled 'Natural Product-based Drug Combination and Its Disease-specific Molecular Regulation (NPCDR)' was thus introduced. This database was unique in (a) providing the comprehensive information of NP-based drug combinations & describing their clinically or experimentally validated therapeutic effect, (b) giving the disease-specific molecular regulation data for a number of NP-based drug combinations, (c) fully referencing all NPs, drugs, regulated molecules/pathways by cross-linking them to the available databases describing their biological or pharmaceutical characteristics. Therefore, NPCDR is expected to have great implications for the future practice of network pharmacology, medical biochemistry, drug design, and medicinal chemistry. This database is now freely accessible without any login requirement at both official (https://idrblab.org/npcdr/) and mirror (http://npcdr.idrblab.net/) sites.
Keyphrases
  • adverse drug
  • drug discovery
  • drug induced
  • healthcare
  • emergency department
  • single molecule
  • transcription factor
  • mesenchymal stem cells
  • deep learning
  • data analysis
  • replacement therapy