2022
DOI: 10.2174/1570163819666220811094019
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Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine Learning and Molecular Docking

Abstract: Background: The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates. Methods: In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potentials drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydr… Show more

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(2 citation statements)
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“…Therefore, there is a constant need for new medications that can safely and efficiently combat SARS‐CoV‐2. Drug design and discovery have benefited from the use of molecular docking to determine how molecules (or ligands) interact with receptors [28–34] …”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Therefore, there is a constant need for new medications that can safely and efficiently combat SARS‐CoV‐2. Drug design and discovery have benefited from the use of molecular docking to determine how molecules (or ligands) interact with receptors [28–34] …”
Section: Introductionmentioning
confidence: 99%
“…Drug design and discovery have benefited from the use of molecular docking to determine how molecules (or ligands) interact with receptors. [28][29][30][31][32][33][34] As a result, In this study we reports the synthesis, characterisation and DFT calculations on the structural properties of the symmetrical azine (VAHD and VNHD).…”
Section: Introductionmentioning
confidence: 99%