Synthesis and structure-activity relationships of 1-substituted 4-(1,2-diphenylethyl)piperazine derivatives having narcotic agonist and antagonist activity

1987 ◽  
Vol 30 (10) ◽  
pp. 1779-1787 ◽  
Author(s):  
Kagayaki Natsuka ◽  
Hideo Nakamura ◽  
Yoshinori Nishikawa ◽  
Toshiyuki Negoro ◽  
Hitoshi Uno ◽  
...  
Keyword(s):  
Antagonist Activity ◽  
Structure Activity Relationships ◽  
Piperazine Derivatives ◽  
Structure Activity ◽  
Agonist And Antagonist

Structure-Activity Relationships of some 1,4-Benzodioxane Aryl-piperazine Derivatives as α-Blocking Agents

1996 ◽  
Vol 329 (10) ◽  
pp. 468-470 ◽  
Author(s):  
Stefano Corsano ◽  
Giovannella Strappaghetti ◽  
Rossana Scapicchi ◽  
Gabriella Marucci
Keyword(s):  
Structure Activity Relationships ◽  
Blocking Agents ◽  
Piperazine Derivatives ◽  
Structure Activity

ChemInform Abstract: Synthesis and Structure-Activity Relationships of Novel Arylalkyl 4-Benzyl Piperazine Derivatives as σ Site Selective Ligands.

ChemInform ◽  
2010 ◽  
Vol 31 (24) ◽  
pp. no-no
Author(s):  
Salome Younes ◽  
Youssef Labssita ◽  
Genevieve Baziard-Mouysset ◽  
Marc Payard ◽  
Marie-Claire Rettori ◽  
...  
Keyword(s):  
Structure Activity Relationships ◽  
Piperazine Derivatives ◽  
Structure Activity ◽  
Selective Ligands ◽  
Site Selective

scholarly journals Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure–Activity Relationship System

2021 ◽  
Vol 22 (19) ◽  
pp. 10821
Author(s):  
Yasunari Matsuzaka ◽  
Shin Totoki ◽  
Kentaro Handa ◽  
Tetsuyoshi Shiota ◽  
Kota Kurosaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Prediction Models ◽  
Three Dimensional ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Antagonist Activity ◽  
Structure Activity ◽  
Agonist And Antagonist

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure–activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system–which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations—we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.

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