Calculation Of Molecular Descriptors Research Articles

Ferrate(VI) oxidation of substituted nitrobenzene compounds: Kinetics, degradation, and oxidized products

Nitrobenzene pollutants are discharged in large quantities into water bodies, posing a serious threat to the ecological environment. However, the effects of the nitrobenzene compounds with different structures on the Fe(VI) degradation process remain unclear. This research selects four different substituted nitrobenzene compounds (1,2-dinitrobenzene (1,2-DB), 4-nitrochlorobenzene (4-NCB), pentachloronitrobenzene (PCT), and 3,4-dinitrochlorobenzene (3,4-DTB)) and explores the oxidative degradation effect of ferrate(VI) based on experimental results and theoretical calculations. The reaction of Fe(VI) (800 μM) with each of the four nitrobenzene compounds (40 μM) reflected second-order reaction kinetics at pH 8: 4-NCB (127.08 M−1s−1) > PCT (113.13 M−1s−1) > 1,2-DB (100.1 M−1s−1) > 3,4-DTB (96.459 M−1s−1). Liquid chromatography time-of-flight mass spectrometry (LC-TOF-MS) analysis detected the main intermediates, which consisted of hydroxylation, ring-opening, coupling, and amine-based products. Free radicals in the reaction system were identified and analyzed, and hydroxyl radicals and carbon dioxide radicals were found. In addition, density functional theory (DFT) was used to explore the degradation mechanisms in the reaction between each of the four substances and hydroxyl radicals, with the active sites of the nitrobenzenes during the Fe(VI) degradation process determined through the calculation of molecular descriptors. Toxicity analysis with ECOSAR software indicated Fe(VI) reduced the toxicity of the nitrobenzenes. This study provides experimental and theoretical support for the reaction mechanism between nitrobenzenes and Fe(VI), as well as further understanding of the degradation mechanism of nitrobenzene compounds.

Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional “QSAR-ready” forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the “QSAR-ready” workflow to generate “MS-ready structures” to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

A DFT study of sulforaphane adsorption on the group III nitrides (B12N12, Al12N12 and Ga12N12) nanocages

In this paper, the adsorption behavior of group III nitrides (B12N12, Al12N12, and Ga12N12) nanocages to sulforaphane (SF) anticancer medicine were studied by density functional theory (DFT). The adsorption energy, solvation energy, desorption time and related quantum molecular descriptors were calculated in neutral and acidic solutions. When the drugs were adsorbed to nanocages, the structure of nanocages and drugs changed after adsorption, indicating that the process was effective adsorption. The adsorption energy and solvation energy of the complexes created after adsorption were negative values, which indicated that the structure of complexes formed by adsorption were stable. According to charge decomposition analysis (CDA) and natural bonding orbitals (NBO), drugs act as charge donors and nanocages act as charge acceptors, so that the charge flows from drugs to nanocages. Thermodynamic calculations demonstrate that drugs adsorption on nanocages is a spontaneous exothermic process. The calculation of quantum molecular descriptors confirmed that drugs adsorption on nanocages increased the chemical reactivity and solubility of drugs, which facilitated its transfer in biological fluids. Both interaction region index (IRI) and topological analysis of atom in molecule (AIM) revealed Van Der Waals interaction between drugs and nanocages. Protonation studies demonstrated that acidic circumstances could improve the polarity of complexes, increase the solvation effect, and boost drugs release in target cancer cells. The results of this work indicate that X12N12(X = B, Al, Ga) nanocages can be used as the delivery vehicle of SF drug. Communicated by Ramaswamy H. Sarma

PyL3dMD: Python LAMMPS 3D molecular descriptors package

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure–property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.Graphical

Bioactive Xanthone C-glycoside Derivatives – QSAR Approach

Introduction. Xanthone glycosides have unique structures and properties. Many efforts focus on the search for C-glycoside derivatives of mangiferin with higher bioavailability. The application of the QSAR approach allows for the optimization of the search for novel xanthone derivatives with the desired characteristics.Aim. Using available descriptors of chemical structure, physical-chemical properties, and biological activity, analyze a sample set of known homologs and analogs of mangiferin to QSAR prognosis bioactivity of new xanthone C-glycosides.Materials and methods. 26 molecules of natural homologs and modified derivatives of mangiferin formed the analyzed sample set. Topological graphs of compounds were constructed using ChemicPen software. ChemicDescript software was used for the calculation of molecular descriptors, including the Balaban index. Physicochemical characteristics of molecules as well as Lipinski's rule criteria were calculated in Molinspiration. The spectrum of the most probable (Pa > 0.7) biological activity of the described compounds were predicted using Pass Online. The software Origin (OriginLab, USA) was used for the graphical representation of the results.Results and discussion. Mangiferin and its natural homologs are the most hydrophilic compounds. The hydrolysis of the C-glycosidic bond, alkylation, acylation, and the introduction of an amino substituent radical into the mangiferin structure led to the increase of its lipophilic properties. The spectrum of the most probable biological activities of the described molecules: antitumor, antioxidant, and cardioprotective effects. The results of ADMET modeling based on the substance-drug similarity criteria showed that only 4 compounds correspond to the rule of five. We proposed the validation model to predict bioactivity from lipophilicity and molecule structure described with Balaban index. The error of prediction obtained in a result of cross-validation turned out to be about less than 3 %.Conclusion. A correlation between the structure and properties of the molecules discussed has been demonstrated. The obtained results can be used for further prediction of the properties of natural and synthetic xanthone C-glycosides and directed synthesis of new active compounds.

Machine learning based dynamic consensus model for predicting blood-brain barrier permeability

The blood-brain barrier (BBB) is an important defence mechanism that restricts disease-causing pathogens and toxins to enter the brain from the bloodstream. In recent years, many in silico methods were proposed for predicting BBB permeability, however, the reliability of these models is questionable due to the smaller and class-imbalance dataset which subsequently leads to a very high false positive rate. In this study, machine learning and deep learning-based predictive models were built using XGboost, Random Forest, Extra-tree classifiers and deep neural network. A dataset of 8153 compounds comprising both the BBB permeable and BBB non-permeable was curated and subjected to calculations of molecular descriptors and fingerprints for generating the features for machine learning and deep learning models. Three balancing techniques were then applied to the dataset to address the class-imbalance issue. A comprehensive comparison among the models showed that the deep neural network model generated on the balanced MACCS fingerprint dataset outperformed with an accuracy of 97.8% and a ROC-AUC score of 0.98 among all the models. Additionally, a dynamic consensus model was prepared with the machine learning models and validated with a benchmark dataset for predicting BBB permeability with higher confidence scores.

Optoelectrical, electronic, and thermodynamic DFT study of a carbon nanoring and its derivative: application as active layer material in organic solar cell performance improvement and nonlinear optics.

Optoelectrical, electronic, and thermodynamic properties of {6}cycloparaphenylene ({6}CPP) and its derivative, N,N-dimethyl-diaza{6}cycloparaphenylene (DMDA{6}CPP), are theoretically investigated in this work. Using the density functional theory (DFT) and time-dependent DFT (TD-DFT) supported by the APFD and B3LYP-D3 functionals and the 6-311++G (d,p) basis set, the new compound, DMDA{6}CPP, revealed an optical band gap [Formula: see text] eV. With the lowest energy transition excited state recorded at [Formula: see text] and associated light-harvesting efficiency (LHE)[Formula: see text], this derivative demonstrates good characteristics as absorber material in organic solar cell (OSC) devices. The analysis of its charge transport abilities exhibits a ratio of hole/electron reorganization energies [Formula: see text], providing very consistent information on good hole transport abilities. Moreover, this derivative of {6}CPP registered amazing linear and NLO properties with polarizability and first-order hyperpolarizability [Formula: see text] a.u and [Formula: see text] a.u, respectively. These values are far higher than those of both para-nitro aniline (p-NA) and urea commonly used as reference molecules in linear and nonlinear optics, positioning this molecule as a perfect candidate for NLO applications and optoelectronics. Molecular atomization energy calculation, spectra analysis, and thermodynamic descriptor calculations have also revealed a very stable structure.

Molecular Descriptors on Line Graphs of Cactus Chains and Rooted Products Graphs

The application of graph theory in the study of molecular physical and chemical properties involves theoretical mathematical chemistry. Atoms, represented by vertices, and edges, represented by bonds between them, are detailed in simple graphs called chemical graphs. The mathematical derivation of the numerical value of a graph is called the molecular descriptor of the graph. Any connected graph wherein no edge is contained in exclusive of a single cycle is called a cactus graph. In the research in this article, expressions for various molecular descriptors of line graph of the graph obtained by the rooted product of the cycle and path graphs are constructed. This article obtained the calculation of molecular descriptors for line graphs of chain ortho cactus and chain para cactus graphs. To predict the biological activity of a compound, the generalized Zagreb index, the first Zagreb index M 1 G , the second Zagreb index M 2 G , the F-index, the general Randic index, the symmetric division, the atom bond connectivity (ABC), and the geometric arithmetic (GA) descriptors are created.

Enantiomeric Separation and Molecular Modelling of Bioactive 4-Aryl-3,4-dihydropyrimidin-2(1H)-one Ester Derivatives on Teicoplanin-Based Chiral Stationary Phase

The enantiomeric separation of 15 racemic 4-aryl-3,4-dihydropyrimidin-2(1H)-one (DHP) alkoxycarbonyl esters, some of which proved to be highly active as A2B adenosine receptor antagonists, was carried out by HPLC on ChirobioticTM TAG, a chiral stationary phase (CSP) bearing teicoplanin aglycone (TAG) as the chiral selector. The racemic compounds were separated under polar organic (PO) conditions. Preliminarily, the same selectands were investigated on three different Pirkle-type CSPs in normal-phase (NP) conditions. A baseline separation was successfully obtained on TAG-based CSPs for the majority of compounds, some of which achieved high enantioselectivity ratios (α > 2) in contrast with the smaller α values (1–1.5) and the lack of baseline resolution observed with the Pirkle-type CSPs. In particular, the racemic tetrazole-fused DHP ester derivatives, namely compounds 8 and 9, were separated on TAG-based HPLC columns with noteworthy α values (8.8 and 6.0, respectively), demonstrating the potential of the method for preparative purposes. A competition experiment, carried out with a racemic analyte (6) by adding N-acetyl-d-alanine (NADA) to the mobile phase, suggested that H-bonding interactions involved in the recognition of the natural dipeptide ligand d-Ala-d-Ala into the TAG cleft should be critical for enantioselective recognition of 4-aryl DHPs by TAG. The X-ray crystal structure of TAG was elucidated at a 0.77 Å resolution, whereas the calculation of molecular descriptors of size, polar, and H-bond interactions, were complemented with molecular docking and molecular dynamics calculations, shedding light on repulsive (steric effects) and attractive (H-bond—polar and apolar) interactions between 4-aryl DHP selectands and TAG chiral selectors.

Оцінка молекулярної мішені адемолу методом хемоінформатики

Актуальність. Глутаматна ексайтотоксичність та внутрішньочерепна гіпертензія являють собою потенційні мішені щодо можливих розробок патогенетичної терапії уражень мозку, зокрема тих, що асоційовані з високими значеннями внутрішньочерепного тиску. Мета. Методами хемоінформатики обґрунтувати внутрішньовенне застосування адемолу, виявити здатність адемолу блокувати β-адренорецептори, а також за критеріями лікоподібності та біодоступності оцінити можливість його проходження через гематоенцефалічний бар’єр (ГЕБ). Матеріали та методи. Всі обчислення молекулярних дескрипторів були зроблені за допомогою програмного комплексу SIB Swiss Institute of Bioinformatics, розрахункової платформи та Molinspiration Cheminformatics v2016.09, доступних on-line. Результати. Молекулярна маса адемолу не перевищує 500, середній показник ліпофільності розрахований за допомогою програмного комплексу [5], знаходиться для наведених сполук у прийнятному діапазоні. Для адемолу величина LogP дорівнює 2,736, що вище, ніж у ремантадину (2,456), однак нижче, ніж у пропранололу (2,967). Визначено загальну площу полярних поверхонь молекул (TPSA), що розраховується на основі методики, опублікованої Ertl et al. [6] у вигляді внесків суми площин атомів О та N та інших, у складі функціональних груп полярних фрагментів. Для предикації проникнення адемолу через ГЕБ використали розраховані in silico дескриптори — усереднену ліпофільність, що виявилася близькою до описаного раніше коефіцієнту ліпофільності у суміші октанолу та фосфатного буфера [9], та TPSA. Кореляція афінітету (LogKi, nM) з полярністю для відомих β-адреноблокаторів та адемолу описується як параболічна поліноміальна функція другого порядку. Висновки. Побудовано модель кореляції афінітету від ліпофільності для ряду β-адреноблокаторів та передбачено афінність адемолу, що наближається до високоафінних неселективних β-адреноблокаторів.

Flame: an open source framework for model development, hosting, and usage in production environments

This article describes Flame, an open source software for building predictive models and supporting their use in production environments. Flame is a web application with a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures since the software supports structural normalization, molecular descriptor calculation, and machine learning model generation using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively. Moreover, Flame implements a mechanism allowing to extend its source code, adding unlimited model customization. Models generated with Flame can be easily exported, facilitating collaborative model development. All models are stored in a model repository supporting model versioning. Models are identified by unique model IDs and include detailed documentation formatted using widely accepted standards. The current version is the result of nearly 3 years of development in collaboration with users from the pharmaceutical industry within the IMI eTRANSAFE project, which aims, among other objectives, to develop high-quality predictive models based on shared legacy data for assessing the safety of drug candidates.

Progress in QSPR modelling methods

<p indent="0mm">With the introduction of Materials Genome Initiative (MGI) in 2011, high-throughput material computation and material prediction have gradually become a hot topic in materials research. MGI has strong guiding significance and high practical application value as the deepening of the concept of environmental protection, low-consumption, faster, and high-throughput. MGI aims to convert the traditional “Trial Experiment” to “Computation to Experiment”, which has become the trend of materials research. There are two main ways to realize the MGI, the first one is the development of quantum chemical calculations and statistical mechanics, while the other one is the establishment of Quantitative Structure-Property Relationship (QSPR). Among them, the QSPR method has been widely used in recent years due to its excellent predictive properties. Combining data-driven and machine learning algorithms, the QSPR method has undergone major changes today, the most prominent of which is the change in the calculation method used in the QSPR method. Reasonable use of calculation methods in QSPR can greatly increase the speed of QSPR model formation and the accuracy of prediction, which is critical to predicting new materials. The calculation methods are the main factors that determine the accuracy and construction speed of the QSPR model. Choosing the best calculation method is very important to improve the prediction accuracy and calculation speed of the QSPR model. Therefore, this article first summarizes the QSPR model, briefly introduces the calculation steps of the QSPR method, focuses on the calculation methods of molecular descriptors and model optimization algorithms in QSPR, and analyzes the advantages and disadvantages of each method. Finally, this article summarizes the application trend of various calculation methods in QSPR in recent years, and concludes as follow: (1) There are many kinds of molecular descriptor calculation software, and most of the software calculation speed is quite fast. At the same time, the calculated number of molecular descriptors is pretty large, which can achieve better calculation results. However, each software still has its own limitations. Researchers can choose one or some software to calculate molecular descriptors according to their needs. (2) The preprocessing algorithms are mainly screening algorithms, including statistical methods, informatics methods, and biological methods. Evolutionary algorithms, which are developed by biological methods, are increasingly being used because of their excellent global screening capabilities. While the number of molecular descriptors obtained by calculation increase significantly, the global influence of molecular descriptors has been gradually emphasized. Therefore, in recent years, evolutionary algorithms have gradually become a popular choice for molecular descriptor screening. (3) The optimization methods mainly include machine learning algorithms, which can be classified into linear methods, nonlinear methods, and hybrid methods. The first two methods have their own advantages for linear methods can show directly the importance of each molecular descriptor and the nonlinear methods’ models are more accurate. In order to improve the efficiency of algorithm parameter calculation, in recent years, more and more global optimization algorithms have been introduced into machine learning algorithms as parameter optimization methods, which forms the third method—Hybrid optimization method.

Modeling the toxicity of pollutants mixtures for risk assessment: a review

The occurrence of contaminants in natural waters is a potential threat to the environment. Since contaminants are commonly present as mixtures, numerous interactions may occur, resulting in lower or, more dangerously, higher toxicity, by comparison with single substances. The toxicity of multicomponent systems can be determined experimentally, but toxicity prediction by suitable models is faster, environmentally friendly and less expensive. Here we review approaches and models, which can be utilized in assessing toxicity of chemical mixtures. In the first part, the assessment of toxicity of chemical mixtures and possible interactions between mixture constituents are discussed. The second part covers conventional modeling, including the simplest, and most common toxicity models, namely concentration addition and independent action models, and derived integrated models. The third part presents advanced toxicity modeling. We review the quantitative structure–activity relationship (QSAR) approach and its elements: calculation of molecular descriptors and their selection with principal component analysis and genetic algorithm. Modeling with artificial neural networks is also discussed. We present hybrid models which combine the fuzzy set theory approach with the conventional concentration addition and independent action models. We conclude that conventional models: concentration addition and independent action model, are still most commonly used; integrated models are more accurate compared to conventional ones, even though their application requires more data; advanced numerical methods such as genetic algorithm, neural networks, and fuzzy set theory give a new perspective on toxicity prediction, and no universal tool for toxicity assessment has been developed so far.

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach

The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the “ocean” of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool (http://mobiosd-hub.com/starpep/), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

Adsorption of non-ionic surfactants on organoclays in drilling fluid investigated by molecular descriptors and Monte Carlo random walk simulations

Non-ionic surfactants have been used as a rheology control additive in drilling fluids to prevent flocculation of solids, such as organoclays, and maintain mud dispersion. Here, the fundamental phenomena involved in non-ionic surfactant adsorption on organoclays and how it affects the rheology of synthetic-based drilling fluids were elucidated by the analysis of molecular descriptors and Monte Carlo simulations. Using the Random Forests machine learning algorithm software, the non-ionic surfactant adsorption on organoclays was found to be affected mainly by the hydrophobicity and molecular shape of hydrophobic chains of non-ionic surfactants. Molecular descriptor calculations indicated that the hydrophobic interaction and van der Waals forces are dominant factors. Then, the Monte Carlo simulations provided the empirical effect of the non-ionic surfactant hydrophobic chains on their self-assembly to organoclays. Molecules with two chains were found to be easily adsorbed to the surface due to molecular size, while molecules with three and four chains occupy more sites and interact with each other more frequently, forming larger clusters. Consequently, non-ionic surfactants with more hydrophobic chains were predicted to improve rheology of drilling fluids and form stable mud emulsions. This approach is useful in predicting the effects of new additive in drilling fluid formulation.

Theoretical calculations of molecular descriptors for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against gastric cancer cell line (MGC-803): DFT, QSAR and docking approaches

sThis work used quantum chemical method via DFT to calculate molecular descriptors for the development of QSAR model to predict bioactivity (IC50- 50% inhibition concentration) of the selected 1, 2, 3-triazole-pyrimidine derivatives against receptor (human gastric cancer cell line, MGC-803). The selected molecular parameters were obtained by B3LYP/6-31G∗∗. QSAR model linked the molecular parameters of the studied compounds to their cytotoxicity and reproduced their observed bioactivities against MGC-803. The calculated IC50 tailored the observed IC50 and greater than standard compound, 5-fluorouracil, suggesting that the developed QSAR model reproduced the observed bioactivity. Statistical analyses (including R2, CV. R2 and gave 0.950, 0.970 and 0.844 respectively) revealed a very good fitness. Molecular docking studies revealed the hydrogen bonding with the amino acid residues in the binding site, as well as ligand conformations which are essential feature for ligand-receptor interactions. Therefore, the methods used in this study are veritable tools that can be employed in pharmacological and medicinal chemistry researches in designing better drugs with improve potency.

QSAR-ANALYSIS OF POLYSUBSTITUTED FUNCTIONALIZED AMINOTHIAZOLES WITH ANTIHYPERTENSIVE ACTIVITY

Background. QSAR analysis is an important tool for the identification of pharmacophore fragments in biologically active substances and helps optimize the search for new effective drugs.&#x0D; Objective. The aim of the study was to determine the molecular descriptors for QSAR analysis of polysubstituted functionalized aminothiazoles as a theoretical basis for purposeful search de novo of potential antihypertensive drugs among the investigated compounds.&#x0D; Methods. Calculation of molecular descriptors and QSAR-models creation was carried out using the Hyper-Chem 7.5 and BuildQSAR packages.&#x0D; Results. The calculation of a number of molecular descriptors (electronic, steric, geometric, energy) was performed for 15 new polysubstituted functionalized aminothiazoles, with established in vivo antihypertensive activity. According to the calculated molecular descriptors and antihypertensive activity parameter, the QSAR models were derived НА = a + b ∙ X1 + c ∙ X2 + d ∙ X3 , where the activity parameter НА is antihypertensive activity and X1, X2, X3 are molecular descriptors.&#x0D; Conclusion. The study of ‘the structure - antihypertensive activity’ relationship for polysubstituted functionalized aminothiazoles was carried out. QSAR analysis revealed that volume, area, lipophilicity, dipole moment, refractivity, polarization of the molecule and energy of the lowest unoccupied molecular orbital have the most significant effect on antihypertensive activity. It was suggested that the attained QSAR-models may have antihypertensive activity within abovementioned row of compounds and can be considered as theoretical basis for de novo design of new potential antihypertensive drugs.

Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches.

Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phosphate pathway (PPP). The G6PD deficiency renders the inability to regenerate glutathione due to lack of Nicotine Adenosine Dinucleotide Phosphate (NADPH) and produces stress conditions that can cause oxidative injury to photoreceptors, retinal cells, and blood barrier function. In this study, we constructed pharmacophore-based models based on the complex of G6PD with compound AG1 (G6PD activator) followed by virtual screening. Fifty-three hit molecules were mapped with core pharmacophore features. We performed molecular descriptor calculation, clustering, and principal component analysis (PCA) to pharmacophore hit molecules and further applied statistical machine learning methods. Optimal performance of pharmacophore modeling and machine learning approaches classified the 53 hits as drug-like (18) and nondrug-like (35) compounds. The drug-like compounds further evaluated our established cheminformatics pipeline (molecular docking and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis). Finally, five lead molecules with different scaffolds were selected by binding energies and in silico ADMET properties. This study proposes that the combination of machine learning methods with traditional structure-based virtual screening can effectively strengthen the ability to find potential G6PD activators used for G6PD deficiency diseases. Moreover, these compounds can be considered as safe agents for further validation studies at the cell level, animal model, and even clinic setting.

QSAR Analysis of Selected Antimicrobial Structures Belonging to Nitro-derivatives of Heterocyclic Compounds

Background: Nitro-derivatives of heterocyclic compounds were used as active agents against pathogenic microorganisms. A set of 4- and 5-nitroimidazole derivatives exhibiting antimicrobial activity was analyzed with the use of Quantitative Structure-Activity Relationships (QSAR) method. The study included compounds used both in documented treatment and those described as experimental. Objective: The purpose of this study was to demonstrate the common and differentiating characteristics of the above-mentioned chemical compounds alike physicochemically as well as pharmacologically based on the quantum chemical calculations and microbiological activity data. Methods: During the study PCA and MLR analysis were performed, as the types of proposed chemometric approach. The semi-empirical and ab initio level of in silico molecular modeling was performed for calculations of molecular descriptors. Results: QSAR models were proposed based on chosen descriptors. The relationship between the nitro-derivatives structure and microbiological activity data was able to class and describe the antimicrobial activity with the use of statistically significant molecular descriptors. Conclusion: The applied chemometric approaches revealed the influential features of the tested structures responsible for the antimicrobial activity of studied nitro-derivatives.

Synthesis, molecular docking and QSAR study of thiazole clubbed pyrazole hybrid as α-amylase inhibitor

In search of potent α-amylase inhibitors, herein we report the synthesis, molecular docking and QSAR study of some thiazole clubbed pyrazole hybrids (TCPH) i.e., 1-((1-phenyl-3-aryl-1H-pyrazole-4-yl)methylene)-2-(4-arylthiazole-2-yl)hydrazine (4a–4r) as an α-amylase inhibitors. Among the different analogues, compounds 4g and 4h were found to be most potent at 50 µg/mL with 89.15% and 88.42% of inhibition. The Monte Carlo optimisation method was applied to build robust QSAR models for the prediction of percentage inhibition of TCPH at different concentration with various statistical parameters. The Simplified Molecular Input Line Entry System (SMILES) was applied to symbolise the molecular structure, descriptor calculation and model development. The role of the index of ideality correlation (IIC) was also studied which revealed a model for split 3 as a leading model with best R2 i.e., 0.9198. The compound 4l at different concentration (TCPH11, TCPH29 and TCPH47) was outside the applicability domain (AD) for the developed QSAR models for split 4 only. The SMILES attributes at three different concentrations were also detected. These attributes are the promoters of percentage increase/decrease in inhibition efficiency of the inhibitors. The docking simulation of most active compounds (4g and 4h) were performed within the active site of Aspergillus oryzae α-amylase (PDB ID: 7TAA) to analyse the binding conformation and interactions responsible for their activity. As a result, it was found that the binding interactions found between 4g, 4h and α-amylase were similar to those responsible for α-amylase inhibition by acarbose.Communicated by Ramaswamy H. Sarma