TY  - JOUR
T1  - Network-Based Insight Analysis of Drugs of China Food and Drug Administration 
  for Potential New Multi-Target Drug Discovery
AU - Bao, Jin-Ku AU - Zhou, Nan AU - Zhang, Jin-Chun AU - Liu, Yong-Xi AU - Yu, Yang AU - Deng, Yuan AU - Feng, Ling AU - Qi, Wei AU - Wu, Chuan-Fang 
JO  - Journal of Animal and Veterinary Advances
VL  - 12
IS  - 17
SP  - 1376
EP  - 1382
PY  - 2013
DA  - 2001/08/19
SN  - 1680-5593
DO  - javaa.2013.1376.1382
UR  - https://makhillpublications.co/view-article.php?doi=javaa.2013.1376.1382
KW  - Drug-target
KW  -computationally
KW  -bipartite graph
KW  -graph theory
KW  -CFDA
AB  - Developing multi-target drugs to obtain potentially innovative medicines has become a trend in the treatment of multifactorial diseases. The open-access resources are used by computational biologists to uncover relationships among various datasets for further drug discovery. In this study, researchers systematically analyzed approved retail drugs of China Food and Drug Administration (CFDA) in terms of biological interactions networks and found that CFDA-approved drugs had significant multi-target properties. To determine the features of these drugs and understand their indication on multi-target drug design, researchers computationally built a bipartite graph composed of drugs and target proteins linked by drug-target binary associations. Furthermore, researchers chose 19 drugs whose target numbers were ≥15 and then integrated human Protein-Protein Interactions (PPIs) datasets from DIP, IntAct, BioGRID, MINT and HPRD to generate a human PPIs network to analyze targets of these drugs. Graph theory analysis identified significant nodes including five multi-target drugs and eight drug targets which indicated that some of the CFDA-approved drugs were potentially valuable for the future development of multi-target drugs.
ER  -