Topological Characterization of the Third Type of Triangular Hex-derived Networks

A topological index is a numerical quantity that defines a chemical descriptor to report several physical, biological and chemical properties of a chemical structure. In recent literature, various degree-based topological indices of a molecular structure are easily calculated by deriving a M-polynomial of that structure. In this paper, we first determine the expression of a M-polynomial of the triangular Hex-derived network of type three of dimension n and then obtain the corresponding degree-based topological indices from the closed form of M-polynomial. In addition, we use Maple software to represent the M-polynomial and the concerned degree-based topological indices pictorially for different dimensions.

Identification of average molecular weight (AMW) as a useful chemical descriptor to discriminate liver injury-inducing drugs

Drug-induced liver injury (DILI) is one of major causes of discontinuing drug development and withdrawing drugs from the market. In this study, we investigated chemical properties associated with DILI using in silico methods, to identify a physicochemical property useful for DILI screening at the early stages of drug development. Total of 652 drugs, including 432 DILI-positive drugs (DILI drugs) and 220 DILI-negative drugs (no-DILI drugs) were selected from Liver Toxicity Knowledge Base of US Food and Drug Administration. Decision tree models were constructed using 2,473 descriptors as explanatory variables. In the final model, the descriptor AMW, representing average molecular weight, was found to be at the first node and showed the highest importance value. With AMW alone, 276 DILI drugs (64%) and 156 no-DILI drugs (71%) were correctly classified. Discrimination with AMW was then performed using therapeutic category information. The performance of discrimination depended on the category and significantly high performance (>0.8 balanced accuracy) was obtained in some categories. Taken together, the present results suggest AMW as a novel descriptor useful for detecting drugs with DILI risk. The information presented may be valuable for the safety assessment of drug candidates at the early stage of drug development.

A New Family of Benzo[h]Chromene Based Azo Dye: Synthesis, In-Silico and DFT Studies with In Vitro Antimicrobial and Antiproliferative Assessment

The high biological activity of the chromene compounds coupled with the intriguing optical features of azo chromophores prompted our desire to construct novel derivatives of chromene incorporating azo moieties 4a-l, which have been prepared via a three-component reaction of 1-naphthalenol-4-[(4-ethoxyphenyl) azo], 1, with the benzaldehyde derivatives and malononitrile. The structural identities of the azo-chromene 4a-l were confirmed on the basis of their spectral data and elemental analysis, and a UV–visible study was performed in a Dimethylformamide (DMF) solution for these molecules. Additionally, the antimicrobial activity was investigated against four human pathogens (Gram-positive and Gram-negative bacteria) and four fungi, employing an agar well diffusion method, with their minimum inhibitory concentrations being reported. Molecules 4a, 4g, and 4h were discovered to be more efficacious against Syncephalastrum racemosum (RCMB 05922) in comparison to the reference drugs, while compounds 4b and 4h demonstrated the highest inhibitory activity against Escherichia coli (E. coli) in evaluation against the reference drugs. Moreover, their cytotoxicity was assessed against three different human cell lines, including human colon carcinoma (HCT-116), human hepatocellular carcinoma (HepG-2), and human breast adenocarcinoma (MCF-7) with a selection of molecules illustrating potency against the HCT-116 and MCF-7 cell lines. Furthermore, the molecular modeling results depicted the binding interactions of the synthesized compounds 3b and 3h in the active site of the E. coli DNA gyrase B enzyme with a clear SAR (structure–activity relationship) analysis. Lastly, the density functional theory’s (DFTs) theoretical calculations were performed to quantify the energy levels of the Frontier Molecular Orbitals (FMOs) and their energy gaps, dipole moments, and molecular electrostatic potentials. These data were utilized in the chemical descriptor estimations to confirm the biological activity.

A quantitative evaluation of computational methods to accelerate the study of alloxazine-derived electroactive compounds for energy storage

Alloxazines are a promising class of organic electroactive compounds for application in aqueous redox flow batteries (ARFBs), whose redox properties need to be tuned further for higher performance. High-throughput computational screening (HTCS) enables rational and time-efficient study of energy storage compounds. We compared the performance of computational chemistry methods, including the force field based molecular mechanics, semi-empirical quantum mechanics, density functional tight binding, and density functional theory, on the basis of their accuracy and computational cost in predicting the redox potentials of alloxazines. Various energy-based descriptors, including the redox reaction energies and the frontier orbital energies of the reactant and product molecules, were considered. We found that the lowest unoccupied molecular orbital (LUMO) energy of the reactant molecules is the best performing chemical descriptor for alloxazines, which is in contrast to other classes of energy storage compounds, such as quinones that we reported earlier. Notably, we present a flexible in silico approach to accelerate both the singly and the HTCS studies, therewithal considering the level of accuracy versus measured electrochemical data, which is readily applicable for the discovery of alloxazine-derived organic compounds for energy storage in ARFBs.

Exfoliation Energy as a Descriptor of MXenes Synthesizability and Surface Chemical Activity

MXenes are two-dimensional nanomaterials isolated from MAX phases by selective extraction of the A component—a p-block element. The MAX exfoliation energy, Eexf, is considered a chemical descriptor of the MXene synthesizability. Here, we show, by density functional theory (DFT) estimations of Eexf values for 486 different MAX phases, that Eexf decreases (i) when MAX is a nitride, (ii) when going along a metal M component d series, (iii) when going down a p-block A element group, and (iv) when having thicker MXenes. Furthermore, Eexf is found to bias, even to govern, the surface chemical activity, evaluated here on the CO2 adsorption strength, so that more unstable MXenes, displaying larger Eexf values, display a stronger attachment of species upon.

Exfoliation Energy as a Descriptor of MXenes Synthesizability and Surface Chemical Activity

MXenes are two-dimensional nanomaterials isolated from MAX phases by the selective extraction of the A component —a p-block element. The MAX phase exfoliation energy, Eexf, is regarded as a chemical descriptor of the MXene synthesizability. Here we show, by density functional theory estimations of the Eexf values for 486 different MAX phases, that Eexf decreases i) when MAX is a nitride, ii) when going along a d series of the metal M component, iii) when going down a group of the p-block A element, as well as iv) when having thicker MXene phases. Furthermore, Eexf is found to bias, even to govern, the surface chemical activity, as evaluated here on the CO2 adsorption strength, so that more unstable MXenes, displaying larger Eexf values, display a stronger attachment of species upon.

Applications of optical descriptors for estimating the amount of sulphur in high viscosity petroleum fractions from the Ashalchinsk oil field

The article proposes a predictive mathematical model to determine the concentration of sulphur in hydrocarbon fractions by the boiling point of the fractions and the refractive index determined by the the sodium yellow line. The model allows for non-linearity of changes in the fractional composition and optical properties with an increase in the amount of sulphur in a multicomponent mixture. Due to the complexity of the multicomponent system, the problem to predict the sulphur concentration was solved using multivariate regression analysis. The model was constructed using a physical and chemical descriptor (boiling point) and an optical descriptor (refractive index). The Ashalchinskoye heavy oil was used as an object for research in this work. The Ashalchinskoye field is one of the most promising fields for industrial development of high-viscosity oil in the territory of Republic of Tatarstan. The authors have analyzed the fractional composition and studied the properties of ashalchinsk oil fractions. Methods for determining the fractional composition were carried out according to the ASTM D 2892-18 Standard Test Method for Distillation of Crude Petroleum (15-Theoretical Plate Column) using the fully computer controlled unit "I-Fisher DIST D-2892/5236 CC" in the range from 200 to 400 oC. The refractive index nD20 was determined using an IRF-454B2M multipurpose laboratory refractometer. The sulfur content in oil fractions was determined in accordance with GOST R 51947-02 and ASTM D 4294 by energy dispersive X-ray fluorescence spectroscopy using a sulphur analyzer RX-360SH manufactured by Tanaka Scientific Limited (Japan); for coke residue in accordance with GOST 2059-95 (ISO 351-96) by the POST-2 apparatus manufactured by Millab (Moscow, Russia). In this paper has been made a comparison between the results obtained from regression model and experimental results were for training and testing samples of high-viscosity oil fractions analyzed. Results of numerical studies for Ashalchinsk high-viscosity oil with a high sulphur content demonstrated very good agreement with the experimental data, which suggest adequacy of mathematical model. The data obtained from the model can be used in the preparation of high-viscosity oils for transportation and processing.

On correlation of hyperbolic volumes of fullerenes with their properties

We observe that fullerene graphs are one-skeletons of polyhedra, which can be realized with all dihedral angles equal to π /2 in a hyperbolic 3-dimensional space. One of the most important invariants of such a polyhedron is its volume. We are referring this volume as a hyperbolic volume of a fullerene. It is known that some topological indices of graphs of chemical compounds serve as strong descriptors and correlate with chemical properties. We demonstrate that hyperbolic volume of fullerenes correlates with few important topological indices and so, hyperbolic volume can serve as a chemical descriptor too. The correlation between hyperbolic volume of fullerene and its Wiener index suggested few conjectures on volumes of hyperbolic polyhedra. These conjectures are confirmed for the initial list of fullerenes.

Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

Neural Message Passing for graphs is a promising and relatively recent approach for applying Machine Learning to networked data. As molecules can be described intrinsically as a molecular graph, it makes sense to apply these techniques to improve molecular property prediction in the field of cheminformatics. We introduce Attention and Edge Memory schemes to the existing message passing neural network framework, and benchmark our approaches against eight different physical–chemical and bioactivity datasets from the literature. We remove the need to introduce a priori knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.

Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction

We introduce Attention and Edge Memory schemes to the existing Message Passing Neural Network framework for graph convolution, and benchmark our approaches against eight different physical-chemical and bioactivity datasets from the literature. We remove the need to introduce <i>a priori</i> knowledge of the task and chemical descriptor calculation by using only fundamental graph-derived properties. Our results consistently perform on-par with other state-of-the-art machine learning approaches, and set a new standard on sparse multi-task virtual screening targets. We also investigate model performance as a function of dataset preprocessing, and make some suggestions regarding hyperparameter selection.