A quantitative study of the structure-activity relationship and molecular docking of 5.6.7-trimethoxy-N-aryl-2-styrylquinolin-4-amines as potential anticancer agents using quantum chemical descriptors and statistical methods

Ghalia, Hadaji El; Amina, Goudzal; Aissouq, Abdellah El; Oussama, Cheddadi; Hicham, El Hamdani; Ouammou, Abdelkrim; Bouachrine, Mohammed

doi:10.1016/j.molstruc.2022.133794

Cited by 6 publications

(9 citation statements)

References 33 publications

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“…: 164.6− 166.1 °C. 1 H NMR (300 MHz, CDCl 3 ): δ = 1.16 (3H, t, J = 7.2 Hz, −CH 3 ), 2.50 (2H, q, J = 7.5 Hz, −CH 2 −), 2.62 (4H, t, J = 5.1 Hz, piperazine), 3.44 (4H, t, J = 5.1 Hz, piperazine), 6.96 (2H, d, J = 9.0 Hz, Ar−H), 7 4.2. Cytotoxicity Test.…”

Section: -(4-(4-ethylpiperazin-1-yl)phenyl)-3-(naphthalen-1-yl)prop-2...mentioning

confidence: 99%

“…The disorder known as cancer, which can arise from any cell and develop anywhere in the body, is spreading across the world. Cancer is the most serious condition, according to statistics and the growth of cancer cells . With an estimated 1.8 million deaths (18%), lung cancer remained the most common type of cancer death in 2020, followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancer.…”

Section: Introductionmentioning

confidence: 99%

“…1 H NMR (300 MHz, DMSO-d 6 ): δ = 2.54 (4H, t, J = 5.0 Hz, piperazine), 3.00 (2H, d, J = 6.5 Hz, Allyl-H), 3.34 (4H, t, J = 5.1 Hz, piperazine), 5.12−5.21 (2H, m, Allyl-H), 5.76−5.88 (1H, m, Allyl-H), 6.87 (2H, d, J = 9.0 Hz, Ar−H), 7.43−7.47 (2H, m, Ar−H), 7.62 (1H, d, J = 15.6 Hz, Ar−H), 7.75−7.81 (4H, m, Ar−H), 7.89 (1H, d, J = 15.6 Hz, Ar−H), 7.97 (3H, d, J = 8.9 Hz, Ar−H) 13. C NMR (75 MHz, DMSOd 6 ): δ = 47.3, 52.7, 61.8, 113.5, 113.5, 118.6, 122.1, 123.8, 126.7, 127.2, 127.8, 128.3, 128.6, 130.3, 130.7, 132.8, 133.4, 134.2, 134.6, 143.2, 154.1, 188.0.…”

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confidence: 99%

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Design, Synthesis, and Biological Evaluation Studies of Novel Naphthalene-Chalcone Hybrids As Antimicrobial, Anticandidal, Anticancer, and VEGFR-2 Inhibitors

et al. 2023

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Cancer is a progressive disease that is frequently encountered worldwide. The incidence of cancer is increasing with the changing living conditions around the world. The side-effect profile of existing drugs and the resistance developing in long-term use increase the need for novel drugs. In addition, cancer patients are not resistant to bacterial and fungal infections due to the suppression of the immune system during the treatment. Rather than adding a new antibacterial or antifungal drug to the current treatment plan, the fact that the drug with anticancer activity has these effects (antibacterial and antifungal) will increase the patient's quality of life. For this purpose, in this study, a series of 10 new naphthalenechalcone derivatives were synthesized and their anticancer-antibacterial-antifungal properties were investigated. Among the compounds, compound 2j showed activity against the A549 cell line with an IC 50 = 7.835 ± 0.598 μM. This compound also has antibacterial and antifungal activity. The apoptotic potential of the compound was measured by flow cytometry and showed apoptotic activity of 14.230%. The compound also showed 58.870% mitochondrial membrane potential. Compound 2j inhibited VEGFR-2 enzyme with IC 50 = 0.098 ± 0.005 μM. Molecular docking studies of the compounds were carried out by in silico methods against VEGFR-2 and caspase-3 enzymes.

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Section: -(4-(4-ethylpiperazin-1-yl)phenyl)-3-(naphthalen-1-yl)prop-2...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Design, Synthesis, and Biological Evaluation Studies of Novel Naphthalene-Chalcone Hybrids As Antimicrobial, Anticandidal, Anticancer, and VEGFR-2 Inhibitors

et al. 2023

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“…El Hassan El Assiri has used multiple linear regression (MLR) and artificial neural networks to predict corrosion inhibitory activity of pyridazine-derivatives [ 24 , 25 ]. H. El Ghalia has also used MLR to predict the anticancer activity of 5.6.7-trimethoxy- N -aryl-2-styrylquinolin-4-amines [ 26 ]. Other methods were also used in QSAR modeling such as partial least squares, principal components regression methods and so on.…”

Section: Introductionmentioning

confidence: 99%

QSAR modeling and molecular docking studies of 2-oxo-1, 2-dihydroquinoline-4- carboxylic acid derivatives as p-glycoprotein inhibitors for combating cancer multidrug resistance

Lahyaoui

Diane

El-Idrissi

et al. 2023

Heliyon

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“…To date, thousands of traditional molecular descriptors have been designed to enable computers to process molecules 17 . However, they are mixed with experience and approximation, which are insufficient for accurately reflecting the information carried by a real molecular structure 18 . Therefore, the traditional descriptors and QSAR methods cannot guarantee the reliability or generalization ability, nor can they obtain accurate, reliable and widely applicable mathematical laws or machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Is theoretical chemistry entering a new era of big data learning and reasoning: deep electron cloud-activity and field-activity relationships

Yang

2022

Preprint

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Chemists have been pursuing general mathematical laws to explain the properties of chemical molecules for a long time. However, the traditional quantitative structure-activity relationship (QSAR) models only provide small pieces of knowledge due to their poor generalization performance and limited application domain. This paper attempts to find a path to realize aunified QSAR that can theoretically predict ANY properties of ANY molecules. A framework combining deep learning, accurate quantum chemistry data and a large number of molecularentities is proposed, and its feasibility is studied. The proposed methods are called deep electron cloud-activity relationships (DECAR) and deep field-activity relationships (DFAR), which consist of three essentials: (1) thousands of molecule entities and activities (even reaching the limit of the current computing resources) as input objects and activity responses; (2) three-dimensional electron cloud density or related field data obtained by accurate density functional theory (DFT) methods as input features; and (3) a deep learning model with three-dimensional convolution layers that can learn these large data. The DECAR and DFAR methods are used to distinguish 977 sweet and 1965 nonsweet molecules, and the classification performance of deep learning models is demonstrated to be significantly better than that oftraditional least squares support vector machine (LS-SVM) models. This work will enable the establishment of an interactive international platform for collecting and sharing the accurate electronic cloud and field data of millions of molecules with annotated activities. We envision the development of hundreds of networks trained for various molecular activities using a large number of molecules, which will become open and shared learning and inference tools for chemists all over the world. These networks will make the best use of our existing knowledge of molecular activity to infer the properties of an increasing number ofunknown molecules and will promote our prediction and understanding of molecular properties with unprecedented strength.

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A quantitative study of the structure-activity relationship and molecular docking of 5.6.7-trimethoxy-N-aryl-2-styrylquinolin-4-amines as potential anticancer agents using quantum chemical descriptors and statistical methods

Cited by 6 publications

References 33 publications

Design, Synthesis, and Biological Evaluation Studies of Novel Naphthalene-Chalcone Hybrids As Antimicrobial, Anticandidal, Anticancer, and VEGFR-2 Inhibitors

Design, Synthesis, and Biological Evaluation Studies of Novel Naphthalene-Chalcone Hybrids As Antimicrobial, Anticandidal, Anticancer, and VEGFR-2 Inhibitors

QSAR modeling and molecular docking studies of 2-oxo-1, 2-dihydroquinoline-4- carboxylic acid derivatives as p-glycoprotein inhibitors for combating cancer multidrug resistance

Is theoretical chemistry entering a new era of big data learning and reasoning: deep electron cloud-activity and field-activity relationships

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