Computation of molecular description of supramolecular Fuchsine model useful in medical data

Supramolecular chemistry is a fascinating field that explores the interactions between molecules to create higher-order structures. In the case of the supramolecular chain of Fuchsine acid, which is a type of dye molecule, several chemical applications are possible. Fuchsine acid helps to make better medicine carriers that deliver drugs where they're needed in the body, making treatments more effective and reducing side effects. It also helps create smart materials like sensors and self-fixing plastics, which are useful in electronics, keeping our environment clean, and making new materials. In sensing and detection, the supramolecular chain of Fuchsine acid utilizes as a sensor or detector for specific analyzes. In drug delivery, the supramolecular chains of Fuchsine acid incorporated into drug delivery systems. In recent years, a common method is linking a graph to a chemical structure and using topological descriptors to study it. This technique is becoming increasingly important over time. Topological descriptors gives very useful information while studying the topology of chemical graph. In this paper, we have computed the 3D structure of supramolecular graph of Fuchsine acid. We have computed an explicit expressions of ABC index, GA index, General Randi c ´ index, first and second Zagreb index, hyper Zagreb index, H-index and F-index of supramolecular structure of Fushine acid.

Connection-Based Zagreb Indices of Polycyclic Aromatic Hydrocarbons Structures

BACKGROUND

Topological indices (TIs) are mathematical formulas that are applied in mathematical chemistry to predict the physical and chemical properties of various chemical structures. In this study, three different types of polycyclic aromatic hydrocarbon structures (PAHs) (i.e., Hexa-peri-hexabenzocoronene, Dodeca-benzo-circumcoronene, and Hexa-cata- hexabenzocoronene) are studied with the help of the different connection number-based Zagreb indices.

METHOD

ϑ = (V(ϑ),E(ϑ)) is used as a graph, where V(ϑ) is a collection of vertices and E(ϑ) is a collection of edges. For a vertex y, ∈V(ϑ), the degree d_ϑ (y), is the number of those vertices that are at a distance of 1 from y and the connection number ρ_ϑ (y) is the number of such vertices that are at a distance of 2 from vertex y.

RESULTS

Theoretical applications of topological indices were described in detail.

CONCLUSION

Finally, we obtained the first and second Zagreb connections as well as the modified first, second, third, and fourth Zagreb connection indices, which were calculated for three different types (Hexa-peri-hexabenzocorone, Dodeca-benzo-circumcoronene, and Hexa-cata-hexabenzocoronene) of polycyclic aromatic hydrocarbon structures.

Computing Analysis of Degree and Connection Based Irregular Indices of Polycyclic Aromatic Hydrocarbons

INTRODUCTION

A graph is supposed to be regular if all vertices have equal degree, otherwise irregular.

MATERIALS AND METHODS

Polycyclic aromatic hydrocarbons are important combusting material and considered as class of carcinogens. These polycyclic aromatic hydrocarbons play an important role in graphitisation of medical science. A topological index is a function that assigns a numerical value to a (molecular) graph which predicts various physical, chemical, biological, thermodynamical and structural properties of (molecular) graphs. An irregular index is a topological index that measures the irregularity of atoms with respect to their bonding for the chemical compounds which are involved in the under studying graphs.

RESULTS AND DISCUSSION

In this paper, we will compute an analysis of distance based irregular indices of polycyclic aromatic hydrocarbons. A comparison among the obtained indices with the help of their numerical values and the 3D presentations is also included. The efficient and steady indices of polycyclic aromatic hydrocarbons are addressed in the form of their irregularities.

CONCLUSION

Connection based study of the molecular graphs is more suitable than the degree based irregularity indices.

Potential of topological descriptors to model the retention of polychlorinated biphenyls in different gas chromatography stationary phases, including ionic liquid-based columns

The aim of this study was to develop a statistical model based on a set of intuitive topological descriptors that will help to determine the influence of the polychlorinated biphenyls (PCBs) structural features on the chromatographic behavior of these analytes in a variety of gas chromatographic stationary phases, including the highly polar ionic liquid (IL)-based SLB-IL76 and SLB-IL60 columns. The model was developed using the stepwise multiple linear regression method, and constructed through several levels of increasing complexity to make evident the relative influence of the selected descriptors. The proposed model was easy to implement and provided similar satisfactory results irrespective of the dependent variables used (i.e., retention index or retention time) or the chromatographic conditions applied (i.e., pseudo-isotherm and programmed temperature) for IL-based phases. The model also allowed the correct prediction of the elution order of selected PCBs in these and other less polar phases evaluated (i.e., SW-10, DB-17, ZB-5 and HT-8). To our knowledge, this is the first models based on topological descriptors described in the literature that provided a satisfactory fitting of the PCB behavior in IL-based phases. Our results indicated that the mechanism governing the chromatographic separation of PCBs in these highly polar columns showed significant differences compared with those observed in other less polar stationary phases.

Topological and quantum molecular descriptors as effective tools for analyzing cytotoxic activity achieved by a series of (diselanediyldibenzene-4,1-diylnide)biscarbamate derivatives

A molecular modeling study has been carried out on a previously reported series of (diselanediyldibenzene-4,1-diylnide)biscarbamate derivatives that show cytotoxic and antiproliferative in vitro activity against MCF-7 human cell line; radical scavenging properties were also confirmed when these compounds were tested for their ability to scavenge DPPH and ABTS radicals. The data obtained allowed us to classify the compounds into two different groups: (a) aliphatic carbamates for which the activity could be related with a first nucleophilic attack (mediated by H₂O, for example) on the selenium atoms of the central scaffold, followed by the release of the alkyl N-(4-selanylphenyl) and N-(4-selenenophenyl)carbamate moieties. Then, a second nucleophilic attack on the carbamate moiety, to yield 4-aminobenzeneselenol and 4-selenenoaniline respectively, which can ultimately be responsible for the activity of the compounds; (b) aromatic carbamates, for which we propose a preferred nucleophilic attack on the carbamate moiety, yielding 4-[(4-aminophenyl)diselanyl]aniline, the common structural fragment for this series, for which we have previously demonstrated its cytotoxic profile. Then, selenium atoms of the central fragment may later undergo a new nucleophilic attack, to yield 4-selenenoaniline and 4-aminobenzeneselenol. The phenolic moieties released in this process may also have a synergistic cytotoxic and redox activity. The data that support this connection include the conformational behavior and the molecular topography of the derivatives which can influence the accessibility of the hydrolysis points, and some quantum descriptors (bond order, atomic charges, total valences, ionization potential, electron affinity, HOMO 0 and LUMO 0 location, etc.) that have been related to the biological activity of the compounds.

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors

The soil sorption partition coefficient logK_oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK_oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK_oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.