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.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.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.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.
The structure-property relationship is important in understanding molecular behaviors and their best-fit areas of applications. 3-(4-hydroxyphenyl) prop-2-en-1-one 4-phenyl Schiff base and some of its derivatives were optimized via the density functional theory with Becke three Lee Yang Parr correlation and 6-31G* basis set. The molecular properties calculated were the energies of the frontier molecular orbitals [highest occupied molecular orbital (EHOMO), lowest unoccupied molecular orbital (ELUMO), energy bandgap (Eg), chemical hardness (η), softness (S) and hyperpolarizabilities (β)]. The electronic transitions were calculated with the time-dependent density functional theory methods, the absorption maxima (λabs), vertical transition energies (ΔEge), oscillator strengths (f) and molecular orbital (MO) components with their percentage contributions were obtained. The anti-microbial efficacy of the molecules was tested against Staphylococcus aureus aminopeptidase S (AmpS) active site to predict the binding affinities. ADMEtox parameters of all the molecules were also investigated. Eg values ranged from 3.13 to 3.95 eV, β values ranged from 1.45 to 5.81×10-30 esu, and their binding affinities ranged from -4.57 to -6.12 kcal/mol, all were more than that of standard drug, streptomycin (-4.31 kcal/mol). The number of hydrogen bond donors and hydrogen bond acceptors were ranged from 1 to 2 and 3.75 to 5.25, respectively. Variations observed from the calculated molecular properties are the result of varying substituent groups. The molecules can be used as nonlinear optical (NLO) materials and also showed potential for being effective against Staphylococcus aureus.
Cancer remains a threat to human existence owing the high number of deaths associated with it.Combating cancer has continued to garner interest from pharmaceutical companies as the resistance and toxicity issues have been observed with the treatment of known drugs.A series of benzimidazole-chalcone derivatives were obtained from literature and investigated for their ability to inhibit MCF-7 Breast Cancer Cell Lines via QSAR and molecular docking techniques.A QSAR model was generated to establish the relationship between molecular descriptors and bioactivity of benzimidazole-chalcone derivatives.The R 2 , adjusted R 2 , Q 2 and R 2 pred values obtained suffice to deem the model fit and reliable.The binding affinities of the lead compounds were obtained via an extra precision docking procedure at the active site of the human serine/threonine-protein kinase receptor, 3FC2.Molecule 21 showed the closest binding affinity (-9.350 kcal/mol) as compared to doxorubicin (-9.305 kcal/mol), and had the best IC50 as compared to other compounds as reported earlier in literature.In-silico ADME and drug-likeness prediction of all the molecules showed good pharmacokinetic properties, bioavailability and non-toxicity.The model generated here could be used in predicting the bioactivity of novel antiproliferative agents.
Abstract Background 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. Method 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 Result 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 charges transfer. All the compounds were found to obey Lipinski's rule with acceptable pharmacokinetic properties. Conclusion 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.
Background: Inhibition activity of the epigenetic readers, such as bromodomain and extra- C terminal domain protein family, is of high significance in many therapeutic applications due to their ability to regulate gene expression as well as the chromatin structure by binding to acetylysine residues. Objectives: In order to effectively and quickly determine the inhibition activity of these compounds for the desired therapeutic application, this work presents a grid search-based extreme learning machine computational intelligence method through which the inhibition activity of forty different compounds of substituted 4-phenylisoquinolinones was determined. Methods: The prediction and generalization capacity of the developed model were assessed using four different error metrics, which include root mean square error, mean absolute error, mean absolute percentage deviation, and correlation coefficient between the measured values and predicted activities. The lead compound (37), together with a kinase inhibitor, LY294002, and a bromodomain and extra-C terminal inhibitor, CPI-0610, was docked with a bromodomain-containing protein 4 bromodomain 1, 6P05. Results: The developed model performed better than the existing model with percentage improvement of 44.48%, 35.08%, 20.44%, and 1.23% on the basis of mean absolute percentage deviation, mean absolute error, root mean square error, and correlation coefficient, respectively. The lead compound has a better binding score than LY294002 and CPI-0610. Conclusion: Implementation of the developed model would help in searching for anti-inflammatory as well as anticancer agents for effective therapeutic application.
Inflammations generate uneasiness. This study adopts quantum mechanical and molecular docking approach to model and explore twenty derivatives of ibuprofen as potential non-steroidal anti-inflammatory drug candidates taking ibuprofen as the standard. Optimization and calculation of the drug-like quantum chemical parameters of the compounds were conducted at DFT/B3LYP/6-31G* level of theory. Binding affinity, interaction and inhibition of the potential drug-candidates with human COX-2 receptor were investigated using molecular docking studies. Pharmacokinetic properties were studied. The drug candidates interact effectively and spontaneously with the COX-2 receptor via hydrogen bonding and π-π stacking with great binding affinity. The energy gap, global hardness and softness, and chemical potential of the derivatives suggest that they are kinetically unstable, more chemically reactive than the parent drug and are effective electron donors. From the pharmacokinetic studies, all the derivatives are not substrates to permeability glycoprotein (suggesting reduced therapeutic failure), not efficiently permeable to skin, can be absorbed by human intestine and can cross the blood brain barrier. Some derivatives are potential CYP1A2, CYP2D6 and CYP3A4 inhibitors. All the ibuprofen derivatives exhibit comparable drug-likeness with standard
Breast cancer is one of the most lethal diseases that has resulted in many deaths in the world. Development of new compounds and repurposing of approved drugs have become very attractive in the field of drug design. Computer-aided drug design has become popular because it is cost effective and time saving. In this work, the molecular descriptors of some amino chalcone derivatives were derived using the density functional theory; some of the optimized molecules were also docked at the active site of a human serine/threonine-protein kinase receptor, 3FC2, to obtain their binding affinities. The potential surface energies for all compounds range from -190.4 kJ/mol to -172.3 kJ/mol for low energy regions and 199.8 kJ/mol to 263.3 kJ/mol for high energy regions indicating that the ligands would bind well with receptors. All compounds have higher binding energy than the standard drug, 5-Fu (-6.19 kcal/mol) when docked into the active site of 3FC2 and their mode of interaction are just like it was in 5-Fu. Our observations are still subject to confirmation via clinical and pre-clinical investigations.
AbstractCancer 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. SarmaKeywords: Anaplastic lymphoma kinasemolecular dynamicsmolecular dockingpair distribution functionDFTcorrosion AcknowledgmentsThe authors would like to appreciate the Theoretical and Computational Chemistry Unit, Adekunle Ajasin University, for resources made available to conduct this research (Monte Carlo, MD simulation with Fe 110). Oyeneyin Oluwatoba Emmanuel acknowledges computational cluster resources at the Center for High Performance Computing (CHPC), Cape Town, South Africa for DFT and MD (ligand-receptor complexes).Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
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