125187-31-7

Basic information

• Product Name: (E)-3-(benzo[d][1,3]dioxol-5-yl)-N-(3,4-dimethoxyphenethyl)acrylamide
• Synonyms: (E)-3-(benzo[d][1,3]dioxol-5-yl)-N-(3,4-dimethoxyphenethyl)acrylamide
• CAS NO: 125187-31-7
• Molecular Weight: 355.39
• Mol File: 125187-31-7.mol
• Article Data: 4

Chemical Properties

• CAS DataBase Reference: (E)-3-(benzo[d][1,3]dioxol-5-yl)-N-(3,4-dimethoxyphenethyl)acrylamide(CAS DataBase Reference)
• NIST Chemistry Reference: (E)-3-(benzo[d][1,3]dioxol-5-yl)-N-(3,4-dimethoxyphenethyl)acrylamide(125187-31-7)
• EPA Substance Registry System: (E)-3-(benzo[d][1,3]dioxol-5-yl)-N-(3,4-dimethoxyphenethyl)acrylamide(125187-31-7)

Safety Data

• Hazardous Substances Data: 125187-31-7(Hazardous Substances Data)

Upstream product

• 2373-80-0 3,4-methylenedioxy-trans-cinnamic acid 
• 1258543-93-9 C₁₃H₁₂O₆ 
• 120-20-7 2-(3,4-dimethoxyphenyl)-ethylamine 
• 96249-87-5 (E)-3-(1,3-benzodioxol-5-yl)acryloyl chloride 

Relevant articles and documents

Synthesis of zanthoxylamide protoalkaloids and their in silico ADME-Tox screening and in vivo toxicity assessment in zebrafish embryos

Inspired by the simple and attractive structure of zanthoxylamide protoalkaloids: armatamide, rubecenamide, lemairamin, rubemamine and zanthosine; isolated from plants of the genus Zanthoxylum. We report the synthesis of a series of 29 substituted N-pheny

Design, synthesis and experimental validation of novel potential chemopreventive agents using random forest and support vector machine binary classifiers

Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemoprevention of cancers.