Structure-activity Models Research Articles

Oxidation Potential of 2,6-Dimethyl-1,4-dihydropyridine Derivatives Estimated by Structure Descriptors

Linear relationships, expressing the electrochemical properties of molecules as functions of structure, give insight into the associated electrochemical process and are a tool for prediction. Many biological activities rely on water-based dissociation, making electrochemical properties a bridge between structure and activity. Motivated by a previous study, a replica is made here on a different dataset in order to validate/invalidate the previously reported results. There are several methods for obtaining structure-based descriptors. Some of the methods have been devised to account for molecular topology, some to account for molecular geometry, and others to account for both. Two methods are involved here to derive structure-based descriptors and further obtain structure–property relationships (FMPI and ChPE). In order to express structure descriptors, both FMPI and ChPE express first the topology of the molecule, using the heavy atoms identity matrix and the heavy atoms adjacency matrix, both square symmetric matrices in the belief that symmetry is one major factor of molecular stability. A set of 2,6-dimethyl-1,4-dihydropyridine derivatives with oxidation peak potentials and coulometrically determined number of electrons experimental data is subjected to the search for structure–activity relationships. Even if the 2,6-dimethyl-1,4-dihydropyridine is a symmetric compound (of Cs point group), their derivatives are generally not symmetric (9 out of 24 are asymmetric). The dataset is subjected to descriptive and inferential statistics in order to filter out the most relevant structure–activity relationship. The geometry is built using three levels of theory (one from molecular mechanics and two others from density functionals, of which one accounts for the interaction with water as solvent). One challenge of picking one out of two reported measured values is dealt with by calculating the likelihood associated with the two choices. Relevant structure–activity models are extracted and discussed. The use of in vivo (in water, SM8 model) models in geometry optimization (from MMFF94 and B3LYP and to M06 + Water SM8) results in a precision gain, but this is, in most of the cases, not statistically significant, and this can be considered a negative result.

Leveraging In Silico Structure-Activity Models to Predict Acute Honey Bee (Apis mellifera) Toxicity for Agrochemicals.

In the realm of crop protection products, ensuring the safety of pollinators stands as a pivotal aspect of advancing sustainable solutions. Extensive research has been dedicated to this crucial topic as well as new approach methodologies in toxicity testing. Hence, within the agricultural and chemical industries, prioritizing pollinator safety remains a constant objective during the development of predictive tools. One of these tools includes computational models like quantitative structure-activity relationships (QSARs) that are valuable in predicting the toxicity of chemicals. This research uses bee toxicity data to develop artificial neural network classification models for predicting honey bee acute toxicity. Bee toxicity data from 1542 compounds were used to develop models; the sensitivity and specificity of the best model were 0.90 and 0.91, respectively. These in silico models can aid in the discovery of next-generation crop protection products. These tools can guide the screening and selection of next-generation crop protection molecules with high margins of safety to pollinators, and candidates with favorable sustainability profiles can be identified at the early discovery stage as precursors to in vivo data generation.

Two novel temperature-based topological indices with strong potential to predict physicochemical properties of polycyclic aromatic hydrocarbons with applications to silicon carbide nanotubes

For a vertex x ∈ V G , the temperature T x of x is defined as T x = d x /n − d x . A topological/graphical index G I is a map GI:∑→R , where ∑ (resp. R ) is the set of chemical graphs (resp. real numbers). Graphical indices are employed in structure-property & structure-activity modeling to predict physicochemical/thermodynamic/bilogical characteristics of a compound. A temperature-based graphical index of a chemical graph G is defined as GI T ≔ ∑edges f(T x , t y ), where f(T x , T y ) is a symmetric 2-variable map. In this paper, we introduce two new novel temperature-based indices known as the reduced reciprocal product-connectivity temperature (RRPT) index and the geometric-arithmetic temperature (GAT) index. The predictive potential of these indices have been investigated by employing them in structure-property modeling of physicochemical properties of polycyclic aromatic hydrocarbons (PAHs). The normal boiling point (bp) and the standard enthalpy of formation ΔHfo are selected as representatives of physicochemical characteristics. Intermolecular & van der Waals kind of interactions have been represented by bp, whereas, ΔHfo advocates for thermal characteristics of a compound. In order to validate the statistical inference, the lower 22 PAHs have been opted as test molecules as their experimental data is also publicly available. We propose a computational method to compute all temperature indices in literature and employ it to compute them for the lower 22 PAHs. Besides all the existing temperature indices, both RRPT & GAT are used in a quality testing to predict bp and ΔHfo for lower PAHs. Our statistical analysis asserts that both RRPT & GAT outperformed all the existing temperature indices for correlating bp and ΔHfo for lower PAHs. Most appropriate data-fitting regression models have been suggested to be linear. Since RRPT has the both of correlation coefficients >0.95, the study implicates its further employability in structure-property modeling. Importantly, our research contributes towards countering proliferation of graphical indices. Applications to well-performing temperature indices to correlate physicochemical characteristics of silicon carbide nanotubes are presented.

Abstract LB334: Mechanisms of endocrine resistance to letrozole, fulvestrant, and palbociclib are associated with increased sensitivity to hexyl-cuban-1-yl biguanide inhibitors of CYP3A4 mediated epoxyeicosatrienoic acid biosynthesis

Abstract Introduction: Eicosanoid mediated mechanisms of hormone therapy resistance in ER+HER2- breast cancer are poorly understood. Cytochrome P450 arachidonic acid (AA) epoxygenase-derived epoxyeicosatrienoic acids (EETs) contribute to breast cancer progression by promoting mitochondrial oxidative phosphorylation (OXPHOS). However, it remains unclear how EETs contribute to hormonal therapy resistance. In this study, the well-known letrozole resistant (LR) MCF-7 AC1 cell line overexpressing aromatase was selected for fulvestrant resistance (FR). A clonal cell line resistant to letrozole and fulvestrant (LR/FR) also exhibited resistance to palbociclib (PR) (MCF-7 AC1 LR/FR/PR). Xenograft tumors of this cell line were resistant to letrozole, fulvestrant, and palbociclib. Structure activity studies and modeling led to the development of CYP3A4 AA epoxygenase inhibiting biguanides hexyl cuban-1-yl biguanide (HCB) and fluorinated hexyl derivatives C5F2-HCB and C6F3-HCB. Fluorination on the hexyl moiety led to broader inhibition of EET regioisomer biosynthesis. These agents were tested for inhibition of proliferation, signaling, and OXPHOS/glycolysis balance in the MCF-7 AC1 LR/FR/PR cell line. Results: In the MCF-7 AC1 LR/FR/PR clonal cell line cyclin D1 and estrogen receptor (ER) expression were undetectable, while cyclin E1, CDK4, CDK6, CYP3A4, and c-MYC were upregulated 11, 40, 13, 10, and 12- fold (n=3, p<0.001) compared to the MCF-7 cell line. LC-MS analysis demonstrated 1.8, 1.4, and 1.2- fold higher total cellular levels of (±)8,9, (±)11,12, and (±)14,15-EET in the MCF-7 AC1 LR/FR/PR cell line compared to the MCF-7 cell line (n=3, p<0.05). C6F3-HCB suppressed cellular levels of EETs: (±)8,9-EET by 55% (n=3, p<0.031), (±)11,12-EET by 53% (n=3, p<0.029), and (±)14,15-EET by 60% (n=3, P<0.0001). The MCF-7 AC1 LR/FR/PR cell line was more potently inhibited by hexyl-cuban-1-yl biguanides compared to the MCF-7 cell line, exhibiting lower IC50 values (MCF-7 AC1 LR/FR/PR IC50 vs. MCF-7 IC50; HCB 4.1 vs. 7.0 uM; for C5F2-HCB 10 vs. 25 uM; C6F3-HCB: 5.4 vs. 12 uM; P values all <0.05). Measurement of spare respiratory capacity revealed that the MCF-7 AC1 LR/FR/PR cell line had 4.2% spare respiratory capacity compared to 34% for MCF-7, indicating a lower OXPHOS reserve for the resistant cells. Additionally, the MCF-7 AC1 LR/FR/PR cell line displayed a 2.4-fold higher extracellular acidification rate (ECAR) than the MCF-7 cell line, indicating a higher glycolysis rate (Warburg effect) in the resistant cells. Conclusion: Selection for fulvestrant resistance of MCF-7 AC1 cells resulted in upregulation of CYP3A4 and biosynthesis of hormone therapy resistance associated EETs while correlating with greater sensitivity to fluorinated hexyl-cuban-1-yl biguanides that inhibit EET biosynthesis. Citation Format: Zhijun Guo, Jianxun Lei, Allison Makovec, Swaathi Jayaraman, John R. Hawse, Carol Lange, Elizabeth Ambrose, Gunda I. Georg, Tony D'Assoro, Goetz P. Matthew, David A. Potter. Mechanisms of endocrine resistance to letrozole, fulvestrant, and palbociclib are associated with increased sensitivity to hexyl-cuban-1-yl biguanide inhibitors of CYP3A4 mediated epoxyeicosatrienoic acid biosynthesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB334.

Clustering of atoms relative to vector space in the Z-matrix coordinate system and 'graphical fingerprint' analysis of 3D pharmacophore structure.

The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure-activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg-Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the 'graphic fingerprint' technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R2ext value is calculated as 0.998.

Models of quantitative relationship “Structure – activity” in performing preliminary toxicological assessment of chemicals

Introduction. In vivo testing of a huge number of chemical compounds is difficult from an ethical point of view, time-consuming, depends on a large number of objects of animal origin and requires large material costs for conducting experiments. Therefore, there is a need for new thinking to optimize the conduct of toxicological studies.
 The purpose of this study is to substantiate the possibility of using structure-activity models in the framework of a preliminary assessment of chemicals toxicity.
 Materials and methods. The study included three groups of chemicals including organothiophosphates, triazoles, and carbamates. The calculation of descriptors based on SMILES, the construction and validation of regression models was carried out using the tools of the Scikit-learn Version 1.2.2 library in an interactive cloud environment working with the Google Colaboratory program code.
 Results. When comparing a number of models for predicting oral toxicity, it was revealed that a model based on decision trees has the best predictive ability for organothiophosphates and triazoles: 70.1% and 69.5% of cases of descriptor changes led to a change in the endpoint value, respectively; a model for predicting carbamate toxicity based on a random forest explains 53.1% of the observed variance common log (1/DL50).
 Limitations. The study is limited to the area of distribution of the obtained mathematical models.
 Conclusion. As the study showed, the constructed models can explain only some part of the studied effect, therefore, models based on the structure-activity relationship should be used exclusively for preliminary assessment of the toxicity of chemicals, as a screening tool.

Predicting the reaction rates between flavonoids and methylglyoxal by combining molecular properties and machine learning

The kinetics of the reaction between methylglyoxal (MGO) and epigallocatechin gallate have been investigated at pH 7.4 and 37 °C, and the kinetic data were combined with previously obtained data of six other flavonoids to develop a model that allows to predict the trapping capacity of MGO based on the molecular properties of the seven flavonoids. The observed data were augmented by using synthetic minority oversampling technique forming a new data set that was used to create the predicting models for the trapping rate constant of MGO by flavonoids via principal component regression (PCR) and back-propagation neural network algorithm, respectively. The PCR model based on the first six principle components was robust and accurate comparing other created models, with an associated root-mean-square error value of 8.02 × 10−7 on the testing set. This work provides quantitative structure-activity models for rapid and accurate prediction of the trapping rate constant of MGO by flavonoids.

Prediction of adverse effects of drug-drug interactions on the cardiovascular system based on the analysis of structure-activity relationships

The co-administration of drugs often leads to drug-drug interactions, which may be accompanied by various adverse drug reactions that pose a threat to the life and health of patients. The effect caused by adverse drug reactions from the cardiovascular system is one of the most significant manifestations of drug-drug interaction. Clinical assessment of adverse drug reactions resulting from drug-drug interaction between all drug pairs used in therapeutic practice is not possible. The purpose of this work was to build models using structure-activity analysis to predict the effects of adverse drug reactions on the cardiovascular system, mediated by interactions between drug pairs when they are taken together. Data on adverse effects resulting from drug-drug interaction were obtained from the DrugBank database. The data on drug pairs that do not cause the corresponding effects, necessary for building accurate structure-activity models, were obtained from the TwoSides database, which contains the results of the analysis of spontaneous reports. Two types of descriptors were used to describe a pair of drug structures: PoSMNA descriptors and probabilistic estimates of the prediction of biological activities obtained using the PASS program. Structure-activity relationships were established using the Random Forest method. Prediction accuracy was calculated by means of a five-fold cross-validation. The highest accuracy values were obtained using PASS probabilistic estimates as descriptors. The area under the ROC curve was 0.94 for bradycardia, 0.96 for tachycardia, 0.90 for arrhythmia, 0.90 for ECG QT prolongation, 0.91 for hypertension, 0.89 for hypotension.

Homology modeling, docking, and ADMET studies of benzoheterocyclic 4-aminoquinolines analogs as inhibitors of Plasmodiumfalciparum

ObjectivesThe ongoing fight against endemic diseases is necessary due to the growing resistance of malarial parasites to widely accessible medications. Thus, there has been an ongoing search for antimalarial medications with improved efficacy. The goal of this study was to develop derivatives of benzoheterocyclic 4-aminoquinolines with enhanced activities and better binding affinities than the original compounds. MethodsThirty-four derivatives of benzoheterocyclic 4-aminoquinolines were docked (using a model of dihydrofolate reductase-thymidylate synthase [DRTS] protein) with Molegro software to identify the compound with the minimum docking score as a design template. The generated quantitative structure–activity model was employed to estimate the activity of the designed derivatives. The derivatives were also docked to determine the most stable derivatives. Furthermore, the designed derivatives were tested for their drug-likeness and pharmacokinetic properties using SwissADME software and pkCSM web application, respectively. ResultsCompound H-014, (N-(7-chloroquinolin-4-yl)-2-(4-methylpiperazin-1-yl)-1,3-benzoxazol-5-amine) with the lowest re-rank score of −115.423 was employed as the design template. Then 10 derivatives were further designed by substituting –OH, –OCH3, –CHO, –F, and -Cl groups at various positions of the template. We found that the designed derivatives had improved activities compared to the template. The docking scores of the designed derivatives were lower than those of the original derivatives. Derivative h-06 (7-methoxy-4-((2-(4-methylpiperazin-1-yl)benzo[d]oxazol-5-yl)amino)quinolin-6-ol) with four hydrogen bonds was identified as the most stable due to its lowest re-rank score (−163.607). While all of the designed derivatives satisfied both the Lipinski and Verber rules, some derivatives such as h-10 (cytochrome P450 1A2 [CYP1A2]); h-05, h-08, h-09, and h-10 [CYP2C19]; and h-03, h-07, h-08, and h-10 [renal organic cation transporter 2 substrate]) showed poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. ConclusionTen derivatives of benzoheterocyclic 4-aminoquinolines were designed with improved efficacies. Derivatives that follow Lipinski and Verber rules and are mostly non-toxic and non-sensitive to the skin can be utilized in the development of effective antimalarial medications.

Identification and structure-activity modeling of ACE inhibitory peptides demonstrating anti-inflammatory effects: Extracted from Antheraea assama and Philosomia ricnii pupae

Traditionally edible silkworm pupae abundantly available in North eastern region of India have a notable protein content and comprises vast scope of bioactive peptides with unrevealed possibilities. Therefore, in this study, peptides derived from Muga and Eri pupae with highest potential to inhibit ACE (Angiotensin converting enzyme) and scavenge free radicals have been characterized, followed by understanding molecular interaction mechanism behind inhibition action and anti-inflammatory potential in endothelial model. This study was proposed to generate most potent purified chromatography fractions comprising ACE-inhibitory peptides, identified to contain total 105 peptides in fraction F4 and 53 peptides in fraction F6, majorly acquired from haemolymph protein in pupae. 96 peptides with length 5–12 amino acids were selected and exposed to peptide-ACE interaction by molecular docking and simulation. Among top 15 peptides, LTSDRDFYVSK, VIELPYGVENR and IYVSDQY acted as effective competitive ACE inhibitor, peptide QTLVPFMK indicated its non-competitive inhibition. Fraction F4 and F6 remarkably alleviated COX-2, iNOS expression along with NO release, IL-6 (interleukin-6), IL-1β (interleukin-1 beta) and TNF-α (Tumor necrosis factor-alpha) generation. Additionally, peptide fractions showed significant potential to curb elevated free radical concentration and induced endogenous antioxidant enzymes expression. This conveyed that Muga and Eri pupae protein derived peptide fractions certainly carried an effective role in modulating the cross talk between ACE activity and inflammation mechanisms, supporting their therapeutic potency.

Transparency in Modeling through Careful Application of OECD's QSAR/QSPR Principles via a Curated Water Solubility Data Set.

The need for careful assembly, training, and validation of quantitative structure-activity/property models (QSAR/QSPR) is more significant than ever as data sets become larger and sophisticated machine learning tools become increasingly ubiquitous and accessible to the scientific community. Regulatory agencies such as the United States Environmental Protection Agency must carefully scrutinize each aspect of a resulting QSAR/QSPR model to determine its potential use in environmental exposure and hazard assessment. Herein, we revisit the goals of the Organisation for Economic Cooperation and Development (OECD) in our application and discuss the validation principles for structure-activity models. We apply these principles to a model for predicting water solubility of organic compounds derived using random forest regression, a common machine learning approach in the QSA/PR literature. Using public sources, we carefully assembled and curated a data set consisting of 10,200 unique chemical structures with associated water solubility measurements. This data set was then used as a focal narrative to methodically consider the OECD's QSA/PR principles and how they can be applied to random forests. Despite some expert, mechanistically informed supervision of descriptor selection to enhance model interpretability, we achieved a model of water solubility with comparable performance to previously published models (5-fold cross validated performance 0.81 R2 and 0.98 RMSE). We hope this work will catalyze a necessary conversation around the importance of cautiously modernizing and explicitly leveraging OECD principles while pursuing state-of-the-art machine learning approaches to derive QSA/PR models suitable for regulatory consideration.

Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking

There is increasing evidence that links mitochondrial off-target effects with organ toxicities. For this reason, predictive strategies need to be developed to identify mitochondrial dysfunction early in the drug discovery process. In this study, as a major mechanism of mitochondrial toxicity, first, the inhibitory activity of 35 compounds against succinate-cytochrome c reductase (SCR) was investigated. This in vitro study led to the generation of consistent experimental data for a diverse range of compounds, including pharmaceutical drugs and fungicides. Next, molecular docking and protein-ligand interaction fingerprinting (PLIF) analysis were used to identify significant residues and protein-ligand interactions for the Qo site of complex III and Q site of complex II. Finally, this data was used for the development of QSAR models using a regression-based approach to highlight structural and chemical features that might be responsible for SCR inhibition. The statistically validated QSAR models from this work highlighted the importance of low aqueous solubility, low ionisation, fewer 6-membered rings and shorter hydrocarbon alkane chains in the molecular structure for increased inhibition of SCR, hence mitochondrial toxicity. PLIF analysis highlighted two key residues for inhibitory activity of the Qo site of complex III: His 161 as H-bond acceptor and Pro 270 for arene interactions. Currently, there are limited structure-activity models published in the scientific literature for the prediction of mitochondrial toxicity. We believe this study helps shed light on the chemical space for the inhibition of mitochondrial electron transport chain (ETC).

HATS5m as an Example of GETAWAY Molecular Descriptor in Assessing the Similarity/Diversity of the Structural Features of 4-Thiazolidinone.

Among the various methods for drug design, the approach using molecular descriptors for quantitative structure–activity relationships (QSAR) bears promise for the prediction of innovative molecular structures with bespoke pharmacological activity. Despite the growing number of successful potential applications, the QSAR models often remain hard to interpret. The difficulty arises from the use of advanced chemometric or machine learning methods on the one hand, and the complexity of molecular descriptors on the other hand. Thus, there is a need to interpret molecular descriptors for identifying the features of molecules crucial for desirable activity. For example, the development of structure–activity modeling of different molecule endpoints confirmed the usefulness of H-GETAWAY (H-GEometry, Topology, and Atom-Weights AssemblY) descriptors in molecular sciences. However, compared with other 3D molecular descriptors, H-GETAWAY interpretation is much more complicated. The present study provides insights into the interpretation of the HATS5m descriptor (H-GETAWAY) concerning the molecular structures of the 4-thiazolidinone derivatives with antitrypanosomal activity. According to the published study, an increase in antitrypanosomal activity is associated with both a decrease and an increase in HATS5m (leverage-weighted autocorrelation with lag 5, weighted by atomic masses) values. The substructure-based method explored how the changes in molecular features affect the HATS5m value. Based on this approach, we proposed substituents that translate into low and high HATS5m. The detailed interpretation of H-GETAWAY descriptors requires the consideration of three elements: weighting scheme, leverages, and the Dirac delta function. Particular attention should be paid to the impact of chemical compounds’ size and shape and the leverage values of individual atoms.

PBPK Modeling to Simulate the Fate of Compounds in Living Organisms.

Pharmacokinetics study the fate of xenobiotics in a living organism. Physiologically based pharmacokinetic (PBPK) models provide realistic descriptions of xenobiotics' absorption, distribution, metabolism, and excretion processes. They model the body as a set of homogeneous compartments representing organs, and their parameters refer to anatomical, physiological, biochemical, and physicochemical entities. They offer a quantitative mechanistic framework to understand and simulate the time-course of the concentration of a substance in various organs and body fluids. These models are well suited for performing extrapolations inherent to toxicology and pharmacology (e.g., between species or doses) and for integrating data obtained from various sources (e.g., in vitro or in vivo experiments, structure-activity models). In this chapter, we describe the practical development and basic use of a PBPK model from model building to model simulations, through implementation with an easily accessible free software.

Appraisal of the human health related toxicological information available on dicyclopentadiene (DCPD) in view of assessing the substance's potential to cause endocrine disruption

Dicyclopentadiene (DCPD) is an olefinic hydrocarbon which is manufactured and imported into the European Union (EU) at greater than 1000 tons per year. Concerns related to fetotoxic effects observed in reproductive toxicity studies at high doses led the REACH registrants to self-classify DCPD as a Category 2 reproductive toxicant under the EU CLP Regulation. DCPD was also reviewed in the European Union in the frame of an ongoing European Chemical Agency (ECHA) Community Rolling Action Plan (CoRAP) procedure and under the French National Strategy on Endocrine Disruptors (SNPE). To elucidate whether the developmental effects may be triggered by an endocrine mode of action, the Lower Olefins Sector Group (LOSG) of the European Chemical Industry Council (CEFIC) formed an ad hoc expert team to review the available scientific information pertaining to the potential endocrine activity and adversity of DCPD. Existing experimental data was complemented with structure activity modelling using ECHA-recommended (Q)SAR tools. Overall, considering the available information from (Q)SAR, mechanistic in vitro and in vivo studies, no indication of endocrine-mediated adversity was found. Hence, the available evidence supports the conclusion that DCPD does not cause developmental toxicity via an endocrine mode of action. Further work is ongoing to support this conclusion.

Reactive dyes for living cells: Applications, artefacts, and some comparisons with textile dyeing

AbstractAn inclusive chemical definition of “reactive” dyeing of textiles is introduced, encompassing the CI Azoic, CI Mordant, CI Reactive, CI Sulphur and CI Vat dye application classes. Such reactive dyeing increases fibre retention of dye and makes application practically possible. The analogous application of dyes and fluorescent probes as microscopic stains in biology and medicine is outlined, focussing on using reactive fluorescent probes with living cells. Parallels with textile dyeing are noted, eg, enhanced probe retention and facilitation of probe application. However, the primary purpose of using reactive probes with live cells is detection of properties of biological systems: to identify biological structures and chemical/biochemical contents; assess biological functions and physicochemical properties; and determine changes in locations of cells and cell components. Problems occurring with such probes are outlined, particularly the problematic character of many standard protocols, and localisation artefacts arising with reactive probes whose reactant and product species are physiochemically significantly different. This latter problem is explored via a case study of possible reactant/product artefacts with probes for reactive oxygen species. Comparison of experimental observations of probe localisations with the localisations predicted using quantitative structure activity (QSAR) modelling indicates that such artefacts can occur with a significant proportion of chemically diverse, widely used, commercially available probes, as well as with experimental compounds reported in the literature. A graphical flowchart is provided to assess possible occurrence of reactant/product artefacts arising with reactive fluorescent probes localising in various organelles of living cells.

Front Cover: Theoretical Investigation of Hydrogen‐Bond‐Assisted Tetradentate N4 Copper(I) Chloride and trans‐1,2‐Peroxodicopper Complexes (Eur. J. Inorg. Chem. 23/2021)

The Front Cover shows an “O2 grasshopper” captured by two “Copper scorpions” using their strong claws and tails (outer-sphere hydrogen bonds). As the key intermediate in the biological oxygenation, the trans-1,2-peroxodicopper adduct was mimicked by a variety of molecular copper complexes. The structure-activity models of hydrogen-bond-assisted tetradentate copper(I) chloride and trans-1,2-peroxodicopper complexes were established, and fundamental insights on the mimics were obtained. More information can be found in the Full Paper by G. Liang and co-workers.

5-(2-Carboxyethenyl)-Isatin Derivatives as Anticancer Agents: QSAR, Molecular Docking and Molecular Dynamic Simulation Analysis

Isatin and its analogues have been shown anticancer activity against various cancer cell lines via restrainting cancer cell proliferation and tumor growth. In this study, five different statistical methods such as MLR, PCR, FA-MLR, GA-MLR, and GA-PLS, were used to aquaire the Quantitative relevance between cytotoxicity activity and 37 isatinstructures. Quantitative structure activity models indicated that topological and gateway parameters have an adequate impact on the cytotoxic activity of the tested molecules. Among the applied QSAR models, GA-PLS and MLR gave significant results with high statistical quality for predicting the inhibitory activity of the molecules. Molecular docking studies of these compounds were also investigated, and promising results were obtained. There was a good correlation between QSAR and docking results. For the validity of the docking studies, molecular dynamics (MD) simulation was also done.

Property predictions demonstrate that structural diversity can improve the performance of polyoxymethylene ethers as potential bio-based diesel fuels

High emissions of particulate matter from diesel engines presents a serious risk to human health and the environment. The addition of oxygenated molecules to diesel fuels has been shown to reduce soot formation during combustion. Polyoxymethylene ethers (POMEs) are a novel class of oxygenated molecules that can be produced from biomass and that have the potential to be used as soot-reducing diesel fuel blendstocks. However, only a few variations of these molecules have been studied thus far, and those that have been characterized present significant disadvantages that could compromise current liquid fuel systems and diesel engines. Using a variety of structure–activity models, we evaluated 67 POMEs to predict the effects of structural variations on important fuel properties. Prediction accuracy was assessed by comparing predictions with measurements for a subset of structures. Nine POME molecules were identified as having potential to reduce soot formation by over 75% compared to conventional diesel fuels while being compatible with current liquid fuel infrastructure, maintaining optimal engine performance, and presenting a minimal risk to the environment. None of these nine POMEs has been previously identified as a potential diesel blendstock. This is the first evaluation of POMEs as a class of molecules and the results guide research on the synthesis, properties, and engine performance of POMEs.

Neighborhood degree sum-based molecular descriptors of fractal and Cayley tree dendrimers.

Topological index is a connection between the chemical structure and the real number that remains invariant under graph isomorphism. In structure–property and structure–activity modeling, topological indices are considered as essential molecular descriptors to predict different physicochemical properties of molecule. Dendrimers are considered to be the most significant, commercially accessible basic components in nanotechnology. In this report, some neighborhood degree sum-based molecular descriptors are obtained for the fractal tree and the Cayley tree dendrimers. Neighborhood M-polynomial yields a family of topological indices for a molecular graph in less time compared to the usual computation from their definitions. Some indices are obtained using neighborhood M-polynomial approach. In addition, some multiplicative neighborhood degree sum-based molecular descriptors are evaluated for fractal and Cayley tree dendrimers. The graphical representations of the outcomes are presented. A comparative study of the findings with some well-known degree-based indices is performed. Usefulness of the descriptors in modeling different properties and activities is discussed.