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This article presents a quantitative structure-activity relationship (QSAR) approach for predicting the acid dissociation constant (pK[Formula: see text]) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing compounds. The model was developed using a diverse dataset of 130 nitrogenous compounds and exhibits excellent predictive performance, with a high coefficient of determination (R[Formula: see text]) of 0.9905, low standard error (s) of 0.3066, and high Fisher statistic (F) of 2142. The model outperforms existing methods, such as Chemaxon software and previous studies, in terms of accuracy and its ability to handle heterogeneous datasets. External validation on pharmaceutical ingredients, dyes, and supramolecular complexes based on cucurbiturils confirms the reliability of the model. To enhance usability, a script-like tool has been developed, providing a streamlined process for users to access the model. This study represents a significant advancement in pK[Formula: see text] prediction, offering valuable insights for drug design and supramolecular system optimization.

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The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical calculations conducted with Psi4 quantum chemistry package was also added to the quantum properties database. Classification results are compared with a baseline of random guesses and predictions obtained with the traditional RDKit generated molecular descriptors. Chosen classification metrics show that results obtained with fragments quantum descriptors fall between results from baseline and those provided by molecular descriptors widely applied in cheminformatics. According to the results, the implementation of principal component analysis, causes a drop in categorization metrics.

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The effect of vicinal molecular groups on the intrinsic acidity of a central guanine residue in short single-stranded DNA models and the potentials exerted by the backbone and the nucleobases on the leaving proton were determined by the fragment molecular orbital (FMO) method, in terms of quantum descriptors (QDs) and pair interaction interfragment decomposition analysis (PIEDA). The acidity of the central guanine moiety decreased with increasing oligonucleotide length, in response to changes by less than 1 eV in the ionization potential, global softness, electrophilicity index, and electronegativity descriptors. The differences in these descriptors were majorly interpreted in terms of the electrostatic influence of the negative charges residing on the backbone of the molecule. Additionally, this electric-field effect was determined explicitly for the displacement of the test hydronium ion to a distance of 250 Å from its original position, resulting in good agreement with calculations of the variation in Gibbs free energies, obtained from physical experiments conducted on the identical oligonucleotide sequences. The reported results are useful for biophysical applications of deoxyriboligonucleotides containing guanine residues in order to induce local negative charges at specific positions in the DNA chain.

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Favipiravir is found to show excellent in-vitro inhibition activity against Nipah virus. To explore the structure-property relationship of Favipiravir, in silico designing of a series of piperazine substituted Favipiravir derivatives are attempted and computational screening has been done to evaluate its bimolecular interactions with Nipah virus. The geometrical features of all the molecules have been addressed from Density Functional Theory calculations. Chemical reactivity descriptor analysis was carried out to understand various reactivity parameters. The drug-likeness properties were estimated by a detailed ADMET study. The binding ability and the mode of binding of these derivatives into the Nipah virus are obtained from molecular docking studies. Our calculations show greater binding ability for the designed inhibitors compared to that of the experimentally reported molecule. Overall, the present work proves to offers new insights and guidelines for synthetic chemists to develop new drugs using piperazine substituted Favipiravir in the treatment of Nipah virus. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42452-020-04051-9.

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The potential toxicity of haloacetic acids (HAAs), common disinfection by products (DBPs), has been widely studied; but their combined effects on freshwater green algae remain poorly understood. The present study was conducted to investigate the toxicological interactions of HAA mixtures in the green alga Raphidocelis subcapitata and predict the DBP mixture toxicities based on concentration addition, independent action, and quantitative structure-activity relationship (QSAR) models. The acute toxicities of 6 HAAs (iodoacetic acid [IAA], bromoacetic acid [BAA], chloroacetic acid [CAA], dichloroacetic acid [DCAA], trichloroacetic acid [TCAA], and tribromoacetic acid [TBAA]) and their 68 binary mixtures to the green algae were analyzed in 96-well microplates. Results reveal that the rank order of the toxicity of individual HAAs is CAA > IAA ≈ BAA > TCAA > DCAA > TBAA. With concentration addition as the reference additive model, the mixture effects are synergetic in 47.1% and antagonistic in 25%, whereas the additive effects are only observed in 27.9% of the experiments. The main components that induce synergism are DCAA, IAA, and BAA; and CAA is the main component that causes antagonism. Prediction by concentration addition and independent action indicates that the 2 models fail to accurately predict 72% mixture toxicity at an effective concentration level of 50%. Modeling the mixtures by QSAR was established by statistically analyzing descriptors for the determination of the relationship between their chemical structures and the negative logarithm of the 50% effective concentration. The additive mixture toxicities are accurately predicted by the QSAR model based on 2 parameters, the octanol-water partition coefficient and the acid dissociation constant (pKa ). The toxicities of synergetic mixtures can be interpreted with the total energy (ET ) and pKa of the mixtures. Dipole moment and ET are the quantum descriptors that influence the antagonistic mixture toxicity. Therefore, in silico modeling may be a useful tool in predicting disinfection by-product mixture toxicities. Environ Toxicol Chem 2021;40:1431-1442. © 2021 SETAC.

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Oxidative stress has been incriminated in the physiopathology of many diseases, such as diabetes, cancer, atherosclerosis, and cardiovascular and neurodegenerative diseases. There is a great interest in developing new antioxidants that could be useful for preventing and treating conditions for which oxidative stress is suggested as the root cause. The thiazolidine-2,4-dione derivatives have been reported to possess various pharmacological activities and the phenol moiety is known as a pharmacophore in many naturally occurring and synthetic antioxidants. Twelve new phenolic derivatives of thiazolidine-2,4-dione were synthesized and physicochemically characterized. The antioxidant capacity of the synthesized compounds was assessed through several in vitro antiradical, electron transfer, and Fe2+ chelation assays. The top polyphenolic compounds 5f and 5l acted as potent antiradical and electron donors, with activity comparable to the reference antioxidants used. The ferrous ion chelation capacity of the newly synthesized compounds was modest. Several quantum descriptors were calculated in order to evaluate their influence on the antioxidant and antiradical properties of the compounds and the chemoselectivity of the radical generation reactions has been evaluated. The correlation with the energetic level of the frontier orbitals partially explained the antioxidant activity, whereas a better correlation was found while evaluating the O-H bond dissociation energy of the phenolic groups.

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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.

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