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. Designing and exploring multi-functional materials are beneficial since they are adaptive to different fields including engineering and pharmaceutics. In this study, we examined the anti-corrosion and anti-cancer potentials of 1-(4-methoxyphenyl)-5-methyl-N'-(2-oxoindolin-3-ylidene)-1H-1,2,3-triazole-4-carbohydrazide (MAC) using computational approaches. The molecular reactivity descriptors and charge distribution parameters of MAC were studied in gas and water at density functional theory (DFT) at B3LYP/6-311++G(d,p) theory level. The binding and mechanism of interaction between MAC and iron surface was studied using Monte Carlo (MC) and molecular dynamics (MD) simulation in hydrochloric acid medium. From the DFT, MC, and MD simulations, it was observed that MAC interacted spontaneously with iron surface essentially via van der Waal and electrostatic interactions. The near-parallel alignment of the corrosion inhibitor on iron plane facilitates its adsorption and isolation of the metal surface from the acidic solution. Further, the compound was docked in the binding pocket of anaplastic lymphoma kinase (ALK: 4FNZ) protein to assess its anti-cancer potential. The binding score, pharmacokinetics, and drug-likeness of MAC were compared with the reference drug (Crizotinib). The MAC displayed binding scores of -5.729 kcal/mol while Crizotinib has -3.904 kcal/mol. MD simulation of the complexes revealed that MAC is more stable and exhibits more favourable hydrogen bonding with the ALK receptor's active site than Crizotinib.Communicated by Ramaswamy H. Sarma.

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. We used structural bioinformatics methods, including density functional theory, molecular docking, molecular dynamic simulation, pharmacokinetic and drug-likeness models, to identify promising EGFR/VEGFR-2 inhibitors from N-(4-methoxyphenyl)-2-[4-(3-oxo-3-phenylprop-1-en-1-yl) phenoxy] acetamide and six of its modified derivatives in light of the lack of effective targets inhibitor Version 14 of Spartan software was used to analyse density functional theory. The Schrodinger software suite 2018's Maestro interface was used for the molecular docking analysis, and the admetSAR and swissADME servers were used for drug-likeness and absorption, distribution, metabolism, excretion, and toxicity. All of the compounds showed strong electronic characteristics. Additionally, all of the tested compounds met the ADMET and drug-likeness requirements without a single instance of Lipinski's rule of five violations. Additionally, the molecules' levels of affinity for the target proteins varied. The highest binding affinities were demonstrated by the MOLb-VEGFR-2 complex (- 9.925 kcal/mol) and the MOLg-EGFR complex (- 5.032 kcal/mol). The interaction of the molecules in the domain of the EGFR and VEGFR-2 receptors was also better understood through molecular dynamic simulation of the complex.

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. In this study, in silico investigations involving a computational method for the discovery of active phytochemicals from Carica papaya against the BRCA-1 gene were carried out. The in silico studies for these phytochemicals datasets as BRCA-1 breast cancer therapeutic agents showed promising results through pharmacokinetics and pharmacodynamics studies. The Carica papaya compounds were found to follow the rule of five and have good bioavailability. The ADMET and drug-likeness screening score of the identified ligands also recognized their potential as a promising drug candidate against BRCA-1 while the DFT also confirm better biological and chemical reactivity of Carica papaya compounds with excellent intra-molecular charge transfer between electron donor and electron acceptor site. The results of the molecular docking provided useful information on possible target-lead interactions, demonstrating that the newly developed leads showed a high affinity for BRCA-1 targets and might be investigated for further research.

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. Additionally, there is a dearth of innovative compounds entering the market as a result of subpar ADMET characteristics. Drug repurposing provides a chance to reenergize the sluggish drug discovery process by identifying new applications for already-approved medications. As this strategy offers a practical way to speed up the process of developing alternative medications for PD. This study used a computer-aided technique to select therapeutic agent(s) from FDA-approved neuropsychiatric/psychotic drugs that can be adopted in the treatment of Parkinson's disease. In the current work, a computational approach via molecular docking, density functional theory (DFT), and pharmacokinetics were used to identify possible (anti)neuropsychiatric/psychotic medications for the treatment of PD. By using molecular docking, about eight (anti)neuropsychiatric/psychotic medications were tested against PARKIN, a key protein in PD. Based on the docking score, the best ligand in the trial was determined. The top hits were compared to the reference ligand levodopa (L-DOPA). A large proportion of the drugs displayed binding affinity that was relatively higher than L-DOPA. Also, DFT analysis confirms the ligand-receptor interactions and the molecular charge transfer. All the compounds were found to obey Lipinski's rule with acceptable pharmacokinetic properties. The current study has revealed the effectiveness of antineuropsychiatric/antipsychotic drugs against PARKIN in the treatment of PD and lumateperone was revealed to be the most promising candidate interacting with PARKIN.

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. RESULTS: The QSAR model of choice satisfied the recommended values and was statistically significant. The R2pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds. CONCLUSION: The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.

Computational Evaluation of Bioactive Compounds from Colocasia affinis 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. Somatic mutations have led to resistance to clinically approved synthetic EGFR inhibitors. Furthermore, synthetic EGFR inhibitors have been associated with several side effects. Thus, there is a need to develop novel EGFR inhibitors with an acceptable biosafety profile and high efficacy. METHODS: Herein, we employed structural bioinformatics and theoretical chemistry techniques via molecular docking, molecular mechanics generalized Born surface area (MM-GBSA) calculation, density functional theory analysis (DFT), and pharmacokinetic study to identify novel EGFR inhibitors. RESULTS: The stringent molecular docking and MM-GBSA calculations identified MET 793, LYS 745, PHE 723, ASP 855, ARG 411, and THR 854 as principal amino acid residues for EGFR-ligands interactions. Furthermore, Colocasia affinis Schott compounds exhibited higher binding energy and more stable interactions than the reference compound (gefitinib). DFT analysis also ascertains better bioactivity and chemical reactivity of C. affinis Schott with favorable intramolecular charge transfer between electron-donor and electron acceptor groups. The pharmacokinetic profile of C. affinis Schott bioactive compounds satisfies Lipinski's rule of five assessment. CONCLUSION: Collectively, C. affinis Schott compounds demonstrated higher inhibitory potentials against EGFR and better pharmacological properties when compared with gefitinib. C. affinis Schott compounds are therefore suggested as promising therapeutic EGFR inhibitors for cancer treatment.

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. RESULTS: The developed QSAR model with predicted (R2pred) of 0.756 showed that the model was fit to be validated parameter for a built model and also proved that the developed model could be used in practical situation, R2 for training set (0.9929) and test set (0.8397) confirmed that the model could successfully predict the activity of new compounds due to its correlation with the experimental activity, the models generated with ELM models showed improved prediction of the activity of the molecules. The lead compounds (22 and 23) had binding energies of -6.327 and -7.232 kcalmol-1 for 22 and 23 respectively and displayed better inhibition at the binding sites of 3POZ when compared with that of the standard drug, chlorambucil (-6.0 kcalmol-1). This could be attributed to the presence of double bonds and the α-ester groups. CONCLUSION: The QSAR and ELM models had good prognostic ability and could be used to predict the bioactivity of novel anti-proliferative drugs.