2018
DOI: 10.1038/s41401-018-0071-1
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Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation

Abstract: With treatment benefits in both the central nervous system and the peripheral system, the medical use of cannabidiol (CBD) has gained increasing popularity. Given that the therapeutic mechanisms of CBD are still vague, the systematic identification of its potential targets, signaling pathways, and their associations with corresponding diseases is of great interest for researchers. In the present work, chemogenomics-knowledgebase systems pharmacology analysis was applied for systematic network studies to genera… Show more

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Cited by 40 publications
(31 citation statements)
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“…The article entitled "Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomicsknowledgebase network analysis and integrated with silico modeling and simulation" systemically describes the potential targets of CBD, the intracellular signaling pathways, and their associations with corresponding diseases. The authors used chemogenomics-knowledge base system and systematic network analysis of pharmacological action to generate CBD-target, targetpathway, and target-disease networks by combining both the results from the in silico analysis and the reported experimental validations [2]. The potential novel targets of CBD are discussed in "GPR3, GPR6, and GPR12 as novel molecular targets: their biological functions and interaction with cannabidiol", in which the authors show that CBD is an inverse agonist for the G-proteincoupled receptors 3, 6, and 12 (GPR3, GPR6, and GPR12) [3].…”
mentioning
confidence: 99%
“…The article entitled "Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomicsknowledgebase network analysis and integrated with silico modeling and simulation" systemically describes the potential targets of CBD, the intracellular signaling pathways, and their associations with corresponding diseases. The authors used chemogenomics-knowledge base system and systematic network analysis of pharmacological action to generate CBD-target, targetpathway, and target-disease networks by combining both the results from the in silico analysis and the reported experimental validations [2]. The potential novel targets of CBD are discussed in "GPR3, GPR6, and GPR12 as novel molecular targets: their biological functions and interaction with cannabidiol", in which the authors show that CBD is an inverse agonist for the G-proteincoupled receptors 3, 6, and 12 (GPR3, GPR6, and GPR12) [3].…”
mentioning
confidence: 99%
“…The D1 receptor model was evaluated with Discrete Optimized Protein Energy (DOPE) [23] measurement and Ramachandran plot [24,25] following our standard process [26]. DOPE, which reflects the energy profiles of individual residues, is a key parameter to determine the quality of a homology model compared to the templates.…”
Section: D2 Receptor and Homology Modeling Of D1 Receptormentioning
confidence: 99%
“…The virtual screening using homology protein model of D1 receptor for the mixed sample of random and active compounds was conducted to determine whether this D1 homology model can have docking process enrich active compounds as top hits. This protocol was successfully applied in previous studies [26,30]. Surflex-Dock Screen, the suite implemented in SYBYL-X 1.3, was employed for the in silico screening.…”
Section: Enrichment Test For D1 Receptor Modelmentioning
confidence: 99%
“…29 – 31 . Due to rapid advancements in the field of chemical and synthetic biology, the network pharmacological paradigm of drug discovery is showing potential to be efficient and effective in exploring the molecular mechanisms of various herbs like Piper longum 32 , herbal formulae’s like Liu-Wei-Di-Huang pill 33 and therapeutic molecules like cannabidiol 34 etc. In the direction of providing better therapeutic effects, concepts of network pharmacology are being utilized to explore potential combinations of drugs 35 .…”
Section: Introductionmentioning
confidence: 99%