Synthesis of 8-Hydroxyquinoline Derivative and Its Zinc Complex

Zhang, Xuejun; Li, Shengling; Zhang, Zhuoqiang; Liao, Chaoqiang; Li, Qiaoling

doi:10.1166/jctn.2015.4177

Cited by 8 publications

(5 citation statements)

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“… 33 Similarly, planar rigid structures with extended conjugation are vital for OLED applications. 34 Most importantly, supramolecular interactions play significant roles in many biological systems and drug molecules. Establishing quinoline derivatives as reliable tectons in supramolecular chemistry is important in this context.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Substituent-Controlled Structural, Supramolecular, and Cytotoxic Properties of a Series of 2-Styryl-8-nitro and 2-Styryl-8-hydroxy Quinolines

et al. 2022

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Styryl quinolines are biologically active compounds with properties largely depending on the substituents on the styryl and quinoline rings. The supramolecular aspects of this class of compounds are rarely explored. In this study, two new series of styryl quinoline derivatives, bearing −OH and −NO 2 groups at the eighthposition of the quinoline ring and −SCH 3 , −OCH 3 , and −Br groups on the styryl ring, have been developed, and their structural, supramolecular, and cytotoxic properties have been analyzed. Crystallographic analyses revealed the exciting substituent-dependent structural and supramolecular features of these compounds. In general, the 8 −OH substituted derivatives ( SA series) exhibited a non-planar molecular geometry having larger dihedral angles (5.75–59.3°) between the planes of the aromatic rings. At the same time, the 8 −NO 2 substituted derivatives ( SB series) exhibited a more or less planar molecular geometry, as revealed by the smaller dihedral angles (1.32–3.45°) between the aromatic rings. Multiple O–H···O, C–H···O, O–H···N, and π–π stacking interactions among the molecules lead to fascinating supramolecular architectures such as hydrogen-bonded triple helices, zig-zag 1D chains, π–π stacked infinite chains, and so forth in their crystal lattice. Hirshfeld surface analyses confirmed the existence of strong π–π stacking and other weak bonding interactions in these compounds. The preliminary cytotoxic properties of SA and SB series compounds were evaluated against the human cervical cancer cell lines (HeLa cells), which further highlighted the roles of functional substituents on the aromatic rings. The SA series compounds with the −OH substituent on the quinoline ring exhibited better cytotoxicity than the SB series compounds with a −NO 2 substituent. Similarly, the electron-withdrawing group −Br on the styryl ring enhanced the cytotoxicity in both series. The IC 50 values were 2.52–4.69 and 2.897–10.37 μM, respectively, for the SA and SB series compounds. Compound S3A having −OH and −Br groups on the quinoline and styryl ring, respectively, exhibited the best IC 50 value of 2.52 μM among all the compounds tested. These findings confirm the relevance of the hydroxyl group in the eighth position of quinoline. In short, the present study attempts to provide a systematic analysis of the effects of aromatic ring substituents on the structural, supramolecular, and cytotoxic properties of styryl quinolines for the first time.

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Section: Introductionmentioning

confidence: 99%

“…For example, face-to-face π–π stacking interactions are beneficial for organic semiconductor applications . Similarly, planar rigid structures with extended conjugation are vital for OLED applications . Most importantly, supramolecular interactions play significant roles in many biological systems and drug molecules.…”

Section: Introductionmentioning

confidence: 99%

Substituent-Controlled Structural, Supramolecular, and Cytotoxic Properties of a Series of 2-Styryl-8-nitro and 2-Styryl-8-hydroxy Quinolines

et al. 2022

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“…The heavy metal ions can be presented as M 2+ -(H 2 O) 6 , M 2+ -(H 2 O) 7 , M 2+ -(H 2 O) 8 , and M 2+ -(H 2 O) 9 clusters in bulk water. 44 The coordination number distribution (CND) probability of Zn(II) complex during adsorption process is given in Table II.…”

Section: Water-zinc Ion Cluster Propertiesmentioning

confidence: 99%

Study of Interaction Energies in Zinc Ion Adsorption on Charged Carbon Nano-Tubes Using Molecular Dynamics Simulation

Dezfoli¹,

Mehrabian²,

Hashemipour³

2013

Jnl of Comp & Theo Nano

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The molecular dynamics simulation was carried out to investigate the dynamic characteristics of adsorption process of Zn(II) in aqueous solution using charged Carbon Nano Tubes (CNTs). Four different carbon nano tubes with carbon atom charges of −0.005e, −0.01e, −0.02e, and −0.03e were selected. During the adsorption process, the van der Waals, electrostatic and total interaction energies between the zinc ion and carbon nano tubes were reported. The results show that the zinc ion was adsorbed to carbon nano tube and the dominant interaction energy was the electrostatic energy between the carbon nano tube and zinc ion. In addition, the rate of adsorption of Zn(II) was first increased and then decreased with increasing carbon nano tube negative surface charge. The zinc ion-water complex properties and reduced density of water around carbon nano tube were also studied. The reduced density profile around carbon nano tubes reveals that the water molecules make high, low, and bulk regions around carbon nano tubes. The high-density water molecules around carbon nano tube with high negative charges, were less flexible than carbon nano tube with lower negative surface charges. This water region can reduce the rate of adsorption process and ion movement toward carbon nano tube.

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“…[1][2][3][4] Basically, kernel methods work by mapping the data from the input space into a high-dimensional (possibly infinite) feature space, which is usually chosen to be a reproducing kernel Hilbert space (RKHS), and then building linear algorithms in the feature space to implement nonlinear counterparts in the input space. The mapping, rather than being given in an explicit form, is determined implicitly by specifying a kernel function, which computes the inner product between each pair of data points in the feature space.…”

Section: Introductionmentioning

confidence: 99%

Choosing General Gaussian Kernel Parameters for Multiclass Pattern Classification

Wang¹,

Zhong²,

Chen³

2015

Jnl of Comp & Theo Nano

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Kernel methods have been established as powerful tools in machine learning and data mining. However, the burden of choosing the appropriate kernel function and its parameters is left to the user. This paper presents an effective general Gaussian kernel parameter selection method for multiclass pattern classification. Unlike the standard single scale Gaussian kernel, the general Gaussian kernel adopts some linear transformations of the input space such that not only the scaling but also the rotation is adapted. We propose to optimize the general Gaussian kernel parameters by maximizing a newly presented kernel evaluation criterion named multiclass kernel polarization (MKP), and this problem is solved through a gradient-based optimization technique. MKP makes full use of all the training samples available and can assess the quality of a kernel in the multiclass classification scenario. The experiments with several UCI data sets demonstrate the effectiveness of the proposed method.

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Synthesis of 8-Hydroxyquinoline Derivative and Its Zinc Complex

Cited by 8 publications

References 0 publications

Substituent-Controlled Structural, Supramolecular, and Cytotoxic Properties of a Series of 2-Styryl-8-nitro and 2-Styryl-8-hydroxy Quinolines

Substituent-Controlled Structural, Supramolecular, and Cytotoxic Properties of a Series of 2-Styryl-8-nitro and 2-Styryl-8-hydroxy Quinolines

Study of Interaction Energies in Zinc Ion Adsorption on Charged Carbon Nano-Tubes Using Molecular Dynamics Simulation

Choosing General Gaussian Kernel Parameters for Multiclass Pattern Classification

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