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QSAR and Molecular Docking Studies of Pyrimidine-Coumarin-Triazole Conjugates as Prospective Anti-Breast Cancer Agents

by Arun Kumar Subramani, Amuthalakshmi Sivaperuman, Ramalakshmi Natarajan, Richie R. Bhandare and Afzal B. Shaik

Molecules 2022, 27(6), 1845; https://doi.org/10.3390/molecules27061845 - 11 Mar 2022

Cited by 20 | Viewed by 4122

Abstract

Cancer is a life-threatening disease and is the second leading cause of death worldwide. Although many drugs are available for the treatment of cancer, survival outcomes are very low. Hence, rapid development of newer anticancer agents is a prime focus of the medicinal chemistry community. Since the recent past, computational methods have been extensively employed for accelerating the drug discovery process. In view of this, in the present study we performed 2D-QSAR (Quantitative Structure-Activity Relationship) analysis of a series of compounds reported with potential anticancer activity against breast cancer cell line MCF7 using QSARINS software. The best four models exhibited a r² value of 0.99. From the generated QSAR equations, a series of pyrimidine-coumarin-triazole conjugates were designed and their MCF7 cell inhibitory activities were predicted using the QSAR equations. Furthermore, molecular docking studies were carried out for the designed compounds using AutoDock Vina against dihydrofolate reductase (DHFR), colchicine and vinblastine binding sites of tubulin, the key enzyme targets in breast cancer. The most active compounds identified through these computational studies will be useful for synthesizing and testing them as prospective novel anti-breast cancer agents. Full article

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9 pages, 892 KiB

Open AccessProceeding Paper

Insecticidal Activity Evaluation of Phenylazo and Dihydropyrrole-Fused Neonicotinoids Against Cowpea Aphids Using the MLR Approach

by Simona Funar-Timofei and Alina Bora

Proceedings 2019, 9(1), 18; https://doi.org/10.3390/ecsoc-22-05664 - 14 Nov 2018

Cited by 1 | Viewed by 1303

Abstract

This paper presents a Quantitative Structure-Activity Relationship (QSAR) study of a series of 24 dihydropyrrole-fused and phenylazo neonicotinoid derivatives, with insecticidal activity tested against Cowpea aphids (Aphis craccivora). In this regard, the conformational search ability of the OMEGA software was employed to model neonicotinoid conformer ensembles, using molecular mechanics calculations based on the 94s variant of the Merck Molecular force field (MMFF94). The minimum energy conformers were used to calculate structural descriptors, which were further related to the insecticidal activity (pLC₅₀ values), using the multiple linear regression (MLR) approach. The genetic algorithm was used for variable selection and several criteria for internal and external model validation. A robust model (r² = 0.880, r²_adj = 0.855, q²_LOO = 0.827, s = 0.2098, F = 34.295) with predictive power (concordance correlation coefficient (CCC)_ext = 0.945, r²_m= 0.824) was obtained, using the QSARINS software. The developed model can be confidently used for the prediction of the insecticidal activity of new chemicals, saving a substantial amount of time and money. Full article

(This article belongs to the Proceedings of 22nd International Electronic Conference on Synthetic Organic Chemistry)

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