DFT Examination of Electronic and Structural Features of Favipiravir for Iron Chelation

Density functional theory calculations were performed to examine electronic and structural features of favipiravir (Fav) for iron (Fe) chelation. Fav was well known for the possible medication of COVID-19; however, its mechanism of action has still been a challenging issue. Therefore, this work was done to provide information regarding the possible action of Fav for participating in the Fe chelation process. To this aim, various types of molecular and atomic descriptors were obtained to discuss the topic of this work. Obtained values of energies indicated different levels of stability for pure Fav compounds, in which such variations were also found for FavFe complexes. Molecular orbital-related features showed a different tendency to contribute to reactions for both pure and complex Fav models, in which changes of the energy levels of molecular orbitals raise the detection function of Fe for Fav compounds. Atomic-scale features also indicated direct and indirect roles of atomic sites for formations of FavFe complex models. As a consequence, the idea of Fe chelation by Fav compound was affirmed regarding the obtained results with providing detailed information for investigating the mechanism of action of Fav in treatment of COVID-19.

Structural analysis of an iron-assisted carbon monolayer for delivery of 2-thiouracil

An idea of employing an iron-assisted carbon (FeC) monolayer for delivery of 2-thiouracil (2TU) was examined in this work by analyzing structural features for singular and bimolecular models. Density functional theory (DFT) calculations were performed for optimizing the structures and evaluating molecular and atomic descriptors for analyzing the models systems. Two bimolecular models were obtained assigning by S-FeC and O-FeC models, in which each of S and O atom of 2TU was relaxed towards the Fe region of FeC surface in the mentioned models, respectively. The results indicated that both models were achievable with slightly more favorability for formation of S-FeC model. The obtained molecular orbital properties revealed the dominant role of FeC monolayer for managing future interactions of attached 2TU, which is indeed a major role for employing nanomaterials for targeted drug delivery purposes. In addition to energies and molecular orbital features, atomic quadrupole coupling constants indicated the benefit of employing FeC monolayer for drug delivery of 2TU.

Quantitative Experimental and Theoretical Research using the DFT Technique on the Structural, UV, Electronic, and FMO Properties of Gammaxene

This investigation gives the exploratory and theoretical purpose behind the distinctive evidence of nuclear structure, expanding and bowing developments, sub-nuclear geometry, powerful UV assessment using density functional theory (DFT) system with a B3LYP/6-311++ basis set. Optical maintenance territory is ideal for fiber optic sensor applications, and the disclosures tend to describe the straightforwardness of γ - HCH. Furthermore, frontier molecular orbital (FMO), UV-Visible NIR, was evaluated and seen as flawless with the exploratory characteristics. The HOMO-LUMO essentialness levels' uniqueness chooses the molecule's engine steadfastness, substance reactivity, compound non-abrasiveness, and hardness. The molecular electrostatic potential (MEP) is a critical mechanical assembly in electrophilic and nucleophilic goals affirmation. To recognize the reflection planes in the GME and to check the crystalline flawlessness of the GME, powder X-beam diffraction examples of the powdered example has been recorded utilizing a Reich Seifert diffractometer with CuKα (λ = 1.5418 Ǻ) radiation at 30 kV, 40 mA. In addition, ADMET boundaries, bioactivity radar, and scores are calculated using Swiss ADME and ADMET pointers to measure sub-atomic descriptors as well as to overview nuclear components.

QSAR Assessing the Efficiency of Antioxidants in the Termination of Radical-Chain Oxidation Processes of Organic Compounds

Using the GUSAR 2013 program, the quantitative structure–antioxidant activity relationship has been studied for 74 phenols, aminophenols, aromatic amines and uracils having lgk7 = 0.01–6.65 (where k7 is the rate constant for the reaction of antioxidants with peroxyl radicals generated upon oxidation). Based on the atomic descriptors (Quantitative Neighborhood of Atoms (QNA) and Multilevel Neighborhoods of Atoms (MNA)) and molecular (topological length, topological volume and lipophilicity) descriptors, we have developed 9 statistically significant QSAR consensus models that demonstrate high accuracy in predicting the lgk7 values for the compounds of training sets and appropriately predict lgk7 for the test samples. Moderate predictive power of these models is demonstrated using metrics of two categories: (1) based on the determination coefficients R2 (R2TSi, R20, Q2(F1), Q2(F2), RmTSi2¯) and based on the concordance correlation coefficient (CCC)); or (2) based on the prediction lgk7 errors (root mean square error (RMSEP), mean absolute error (MAE) and standard deviation (S.D.)) The RBF-SCR method has been used for selecting the descriptors. Our theoretical prognosis of the lgk7 for 8-PPDA, a known antioxidant, based on the consensus models well agrees with the experimental value measure in the present work. Thus, the algorithms for calculating the descriptors implemented in the GUSAR 2013 program allow simulating kinetic parameters of the reactions underling the liquid-phase oxidation of hydrocarbons.

Liquid water contains the building blocks of diverse ice phases

Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases.