Insight into the corrosion inhibiting potential and anticancer activity of 1-(4-methoxyphenyl)-5-methyl-N'-(2-oxoindolin-3-ylidene)-1H-1,2,3-triazole-4-carbohydrazide via computational approaches.

Cancer is a major health concern globally. Orthodox and traditional medicine have actively been explored to manage this disease. Also, corrosion is a natural catastrophe that weakens and deteriorates metallic structures and their alloys causing major structural failures and severe economic implications.

Quantum evaluation and therapeutic activity of (E)-N-(4-methoxyphenyl)-2-(4-(3-oxo-3-phenylprop-1-en-1-yl) phenoxy)acetamide and its modified derivatives against EGFR and VEGFR-2 in the treatment of triple-negative cancer via in silico approach.

The most dangerous subtype of breast cancer, triple-negative breast cancer (TNBC), accounts for 25% of all breast cancer-related deaths and 15% of all breast cancer cases. TNBC is distinguished by the lack of immunohistochemical expression of HER2, progesterone receptors, or oestrogen receptors. Although it has been reported that upregulation of EGFR and VEGFR-2 is associated with TNBC progression, no proven effective targeted therapy exists at this time.

Discovery of Potential Phytochemicals from Carica papaya Targeting BRCA-1 in Breast Cancer Treatment.

The BRCA1 and BRCA2 are genes that encode a protein that ensures the integrity of DNA and prevents the unregulated cells from proliferating. Mutations in the sequence of these genes are associated with the birth of inherited breast cancers. The research for possible human breast cancer treatment remains a vital step in the drug development process.

Discovery of Small Molecule PARKIN Activator from Antipsychotic/Anti-neuropsychiatric Drugs as Therapeutics for PD: an In Silico Repurposing Approach.

Although there is presently no cure for Parkinson's disease (PD), the available therapies are only able to lessen symptoms and preserve the quality of life. Around 10 million people globally had PD as of 2020. The widely used standard drug has recently been revealed to have several negative effects.

Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine Learning and Molecular Docking.

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 potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target.

Computational Evaluation of Bioactive Compounds from Schott as a Novel EGFR Inhibitor for Cancer Treatment.

Introduction: Epidermal growth factor receptor (EGFR) is a transmembrane protein that belongs to the ErbB/HER-family of tyrosine kinase receptors. Somatic mutations and overexpression of EGFR have been reported to play a vital role in cancer cell development and progression, including cell proliferation, differentiation, angiogenesis, apoptosis, and metastatic spread. Hence, EGFR is an important therapeutic target for the treatment of various types of epithelial cancers.

Predicting the bioactivity of 2-alkoxycarbonylallyl esters as potential antiproliferative agents against pancreatic cancer (MiaPaCa-2) cell lines: GFA-based QSAR and ELM-based models with molecular docking.

Background: The number of cancer-related deaths is on the increase, combating this deadly disease has proved difficult owing to resistance and some serious side effects associated with drugs used to combat it. Therefore, scientists continue to probe into the mechanism of action of cancer cells and designing novel drugs that could combat this disease more safely and effectively. Here, we developed a genetic function approximation model to predict the bioactivity of some 2-alkoxyecarbonyl esters and probed into the mode of interaction of these molecules with an epidermal growth factor receptor (3POZ) using the three-dimensional quantitative structure activity relationship (QSAR), extreme learning machine (ELM), and molecular docking techniques.