Pub Date : 2024-03-19DOI: 10.1007/s12039-024-02259-5
Andrés Gonzalez-Oñate, Rodolfo Quevedo
Indirect reductive amination of aromatic aldehydes was studied in this work; aqueous ammonia was used as a nitrogen source. The results showed that aromatic aldehydes’ reaction with aqueous ammonia produced compounds known as hydrobenzamides (N,N´-(phenylmethylene)bis(1-phenylmethanimines), having good yield. The reaction of hydrobenzamides with sodium borohydride reduced both imine and aminal carbons and produced a primary and secondary benzylamine mixture. This article analyses such behaviour and proposes a possible mechanism for explaining such transformation.
Graphical Abstract
The reaction of aromatic aldehydes with aqueous ammonia produced compounds known as hydrobenzamides (N,N´-(phenylmethylene)bis(1-phenylmethanimines). The reaction of hydrobenzamides with sodium borohydride reduced both imine and aminal carbons and produced a primary and secondary benzylamine mixture.
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The quantitative structure-property relationship (QSPR) technique is used to gauge the n-octanol/water partition coefficient (log KOW) and enthalpy of vaporization (∆vapHm) of 133 Polychlorinated Biphenyls (PCBs) using conceptual density functional theory (CDFT)-based global reactivity and information-theory (IT) based parameters. Regression models are established using linear and multi-linear relationships to correlate the observed physicochemical properties of PCBs with the predicted ones. The study explored the significance of CDFT and IT descriptors, and based on the calculation of Pearson correlation coefficient values, the selection of suitable descriptors is made for successful QSPR models of selected PCBs. It is found that some of the CDFT parameters are highly correlated with the IT parameters, as suggested by their high Pearson correlation coefficient values for PCB systems. The regression model generated using the descriptors IG, g1, g2, EA, η for predicting log KOW and IF, g3, η, SS, SGBP for predicting ∆vapHm gives R2 value of 0.9342 and 0.8662, respectively, for the selected 133 PCB congeners. Furthermore, to verify the descriptor selection, a machine learning approach is also used to develop QSPR models in this study.
Graphical Abstract
QSPR modelling using CDFT and information theory-based descriptors for predicting n-octanol/water partition coefficient and enthalpy of vaporization for the selected PCBs
{"title":"QSPR models for n-octanol/water partition coefficient and enthalpy of vaporization using CDFT and information theory-based descriptors","authors":"Arpita Poddar, Akshay Chordia, Pratim Kumar Chattaraj","doi":"10.1007/s12039-024-02250-0","DOIUrl":"10.1007/s12039-024-02250-0","url":null,"abstract":"<div><p>The quantitative structure-property relationship (QSPR) technique is used to gauge the n-octanol/water partition coefficient (log <i>K</i><sub>OW</sub>) and enthalpy of vaporization (<i>∆</i><sub>vap</sub><i>H</i><sub>m</sub>) of 133 Polychlorinated Biphenyls (PCBs) using conceptual density functional theory (CDFT)-based global reactivity and information-theory (IT) based parameters. Regression models are established using linear and multi-linear relationships to correlate the observed physicochemical properties of PCBs with the predicted ones. The study explored the significance of CDFT and IT descriptors, and based on the calculation of Pearson correlation coefficient values, the selection of suitable descriptors is made for successful QSPR models of selected PCBs. It is found that some of the CDFT parameters are highly correlated with the IT parameters, as suggested by their high Pearson correlation coefficient values for PCB systems. The regression model generated using the descriptors <i>I</i><sub><i>G</i></sub>, <i>g</i><sub>1</sub>, <i>g</i><sub>2</sub>, <i>EA</i>, <i>η</i> for predicting log <i>K</i><sub>OW</sub> and <i>I</i><sub><i>F</i></sub><i>, g</i><sub>3</sub>, <i>η</i>, <i>S</i><sub><i>S</i></sub>, <i>S</i><sub><i>GBP</i></sub> for predicting <i>∆</i><sub>vap</sub><i>H</i><sub>m</sub> gives <i>R</i><sup>2</sup> value of 0.9342 and 0.8662, respectively, for the selected 133 PCB congeners. Furthermore, to verify the descriptor selection, a machine learning approach is also used to develop QSPR models in this study.</p><h3>Graphical Abstract</h3><p>QSPR modelling using CDFT and information theory-based descriptors for predicting n-octanol/water partition coefficient and enthalpy of vaporization for the selected PCBs\u0000</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":616,"journal":{"name":"Journal of Chemical Sciences","volume":"136 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140148440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.1007/s12039-024-02254-w
Elif Aydinli, Zeyad Adil Hameed, Haydar Goksu, Sevki Adem
Synthesis of 5-substituted-1H-tetrazole derivatives from aryl aldehydes under the influence of Palladium nanoparticles entrapped in aluminum hydroxide matrix (Pd/AlO(OH) NPs) was carried out in ethanol for 3-6 h. The use of the catalyst in this synthesis is the first. Sodium azide and malononitrile used in the reaction are chemical compounds required in the synthesis of tetrazoles. The reactions were concluded with good yields under thermal conditions. In the reactions, twelve derivatives were synthesized. The synthesized compounds were characterized by IR, 1H, and 13C NMR. The olefinic proton's signal, which is around 8.5 ppm, reveals the formation of the tetrazole ring. The tyrosinase enzyme activity for each synthesized derivative was examined, and the results were recorded. According to the results obtained, all tetrazole derivatives were found to be effective compounds for tyrosinase enzyme inhibition. 3-(3,4-dichlorophenyl)-2-(1H-tetrazol-5-yl)acrylonitrile (2k) with two chloride groups at the meta and para position of the phenyl ring seems to be the most potent tyrosinase inhibitor with an IC50 value of 45 µM.