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Polymers, drugs, and almost all chemical or biochemical compounds are frequently modeled as diverse ω -cyclic, acyclic, bipartite, and polygonal shapes and regular graphs. Molecular descriptors (topological indices) are the numerical quantities and computed from the molecular graph Γ (2D lattice). These descriptors are highly significant in quantitative structure-property or activity relationship (QSPR and QSAR) modeling that provides the theoretical and the optimal basis to expensive experimental drug design. In this paper, we study three isomeric natural polymers of glucose (polysaccharides), namely, cellulose, glycogen, and amylopectin (starch), having promising pharmaceutical applications, exceptional properties, and fascinating molecular structures. We intend to investigate and compute various closed-form formulas such as ABC , GA , sum-connectivity χ − 1 / 2 , ABC 4 , GA 5 , and Sanskruti indices for the aforementioned macromolecules. Also, we present the closed-form formulas for the first, second, modified, and augmented Zagreb indices, inverse and general Randić indices, and symmetric division deg, harmonic, and inverse sum indices. Furthermore, we provide a comparative analysis using 3D graphs for these families of macromolecules to clarify their nature.

Background Targeting viral cell entry proteins is an emerging therapeutic strategy for inhibiting the first stage of SARS-CoV-2 infection. In this study, 106 bioactive terpenoids from African medicinal plants were screened through molecular docking analysis against human angiotensin-converting enzyme 2 (hACE2), human transmembrane protease serine 2 (TMPRSS2), and the spike (S) proteins of SARS-CoV-2, SARS-CoV, and MERS-CoV. In silico absorption-distribution-metabolism-excretion-toxicity (ADMET) and drug-likeness prediction, molecular dynamics (MD) simulation, binding free energy calculations, and clustering analysis of MD simulation trajectories were performed on the top docked terpenoids to respective protein targets. Results The results revealed eight terpenoids with high binding tendencies to the catalytic residues of different targets. Two pentacyclic terpenoids (24-methylene cycloartenol and isoiguesteri) interacted with the hACE2 binding hotspots for the SARS-CoV-2 spike protein, while the abietane diterpenes were found accommodated within the S1-specificity pocket, interacting strongly with the active site residues TMPRSS2. 3-benzoylhosloppone and cucurbitacin interacted with the RBD and S2 subunit of SARS-CoV-2 spike protein respectively. These interactions were preserved in a simulated dynamic environment, thereby, demonstrating high structural stability. The MM-GBSA binding free energy calculations corroborated the docking interactions. The top docked terpenoids showed favorable drug-likeness and ADMET properties over a wide range of molecular descriptors. Conclusion The identified terpenoids from this study provides core structure that can be exploited for further lead optimization to design drugs against SARS-CoV-2 cell-mediated entry proteins. They are therefore recommended for further in vitro and in vivo studies towards developing entry inhibitors against the ongoing COVID-19 pandemic.

Background The emergence of high resistance and toxicity of the existing anti-breast cancer drugs have demanded the need to design new drugs with improved activities against breast cancer. A computational technique incorporating quantitative structure–activity relationship and virtual template-based design was carried out to evaluate thirty-four compounds from derivatives of thiophene, pyrimidine, coumarin, pyrazole and pyridine with anti-breast cancer activities. The chemical structures of the compounds were drawn with chem draw v.12.0.2 and they were optimized using Spartan 14 software. The molecular descriptors were calculated with the aid of PaDel descriptor software. The dataset was curated and then divided into training and test set that was used to generate and validate the model. Results The first out of the four models generated was chosen as the paramount model with statistical validations of R2 = 0.9847, $$R_{{{\text{adj}}}}^{2}$$ R adj 2 = 0.9814, $$Q_{{{\text{cv}}}}^{2}$$ Q cv 2 = 0.9763, min expt. error for non-significant LOF (95%) = 0.0679, an external validation $$R_{{{\text{test}}}}^{2}$$ R test 2 of 0.8240 and coefficient of Y-randomization ($${\text{cR}}_{{\text{p}}}^{2}$$ cR p 2 ) = 0.8200, which confirm the robustness of the model. Conclusions The high predictive power of the generated model describes the models’ reliability and the designed compounds pointed out compound 2 with pGI50 = 4.2504 as the best designed compound to inhibit breast cancer, compared to its co-designed compounds and the template. The results of this research provide vital information to the pharmaceutical chemists and the pharmacologist in the course of developing new breast cancer drugs.

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and β-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R2 = 0.8688, q2 = 0.8600) and BACE1 (R2 = 0.8177, q2 = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r2 = 0.8469, r2pred = 0.8138) and BACE1 (r2 = 0.7805, r2pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer’s lead molecules.

Systematic quantitative structure-retention relationship studies of nucleic acid bases were carried out by the combined use of multivariate analysis and experimental chromatographic technique. The results revealed a multiple linear relationship between the chro- matographic retention and the molecular structural parameters yield- ing a regression R 2 value of 0.8113 (cross-validated Q 2 = 0.6945). Five molecular descriptors, viz., moment of inertia (Ix,Iy and Iz), molar volume, and polar surface area, are able to account for the retention behavior of the compounds. Principal component analysis and factor analysis results indicate that the descriptors moment of inertia and molar volume have a primary influence on the chromato- graphic retention. The results provide useful insights for the future experimental and theoretical studies on the medicinal research of nucleic acid-base compounds.

In the present study, quantitative structure-property relationship (QSPR) models were used for modeling and predicting retention index (RI) values of chemical composition in Polygonum minus essenti...

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.

The relevance of the study of 1,2,4-triazole derivatives with pyrimidine fragment is due to the synthesis of potential broad-spectrum antibacterial drugs, low molecular weight inducers of interferon, and antitumor agents, search for molecular descriptors of their structure, important for establishing patterns “structure – biological activity”. The aim of the work is a computer search for the antibacterial action of new hybrids of 1,2,4-triazole-3(2H)-thiol with a pyrimidine fragment in relation to 5 test cultures, to establish the dependence of “structure – action”. Materials and methods. For an in-depth study of the antibacterial activity of derivatives of 1,2,4-triazole-3(2H)-thiol hybrids with a pyrimidine fragment, 4 test cultures of museum strains of gram-positive, gram-negative bacteria and one species of fungi were selected. In silico studies were performed using regression and classification QSAR models. Results. Derivatives of 1,2,4-triazole-3(2H)-thiol hybrids with a pyrimidine moiety showed high antibacterial activity against gram-negative microorganisms (E. coli, P. aeruginosa). The obtained experimental results allowed to establish not only the role of the main structural features of the compounds in the manifestation of antimicrobial properties, but also to evaluate the effectiveness of the created classification and regression QSAR models. Based on the presented parameters for individual predictive QSAR models, it is possible to conclude about the effectiveness, stability and feasibility of using these models to search for new S-derivatives (1,2,4-triazole-3(2H)-yl)methyl)thiopyrimidines as promising antimicrobial agents. Conclusions. It was found that the studied derivatives of hybrids of 1,2,4-triazole-3(2H)-thiol with a pyrimidine fragment showed high antibacterial activity against gram-negative microorganisms. The developed QSAR classification models based on the percentage of correctly predicted compounds (70 %) are the most effective in comparison with regression (50 %) for the search for new antimicrobial agents in a number of derivatives of hybrids 1,2,4 triazole-3(2H)-thiol with pyrimidine fragment.

In this work, we used the quantum density theory (DFT), B3LYP / 6-311G (d, p) to establish a QSAR (Quantitative Structure Activity Relationships) model on a series of molecules derived from 2-thioarylalkyl-1H -Benzimidazole. This model is built with molecular descriptors and anthelmintic activities against the haemonchus contortus. The statistical indicators of this model are: the coefficient of determination R2, a standard deviation S, the Fisher coefficient F and the cross-validation coefficient Q2cv. The statistical parameters of the model are efficient. The quantum descriptors responsible for the anthelmintic activity of 2-thioarylalkyl-1H-Benzimidazole derivatives are the dipole moment (μ), the energy of the highest occupied orbital (EHOMO), the smallest negative charge of the molecule (q-). The acceptance criterion of Eriksson et al. used for the test series is verified. For the external validation, the values ​​of the ratio of theoretical activity and experimental activity tends to unity.

Avermectins have been effectively used in medicine, veterinary medicine, and agriculture as antiparasitic agents for many years. However, there are still no reliable data on the main ecotoxicological characteristics of most individual avermectins. Although many QSAR models have been proposed to describe the acute toxicity of organic compounds towards Tetrahymena pyriformis (T. pyriformis), avermectins are outside the applicability domain of these models. The influence of the molecular structures of various organic compounds on the acute toxicity towards T. pyriformis was studied using the OCHEM web platform (https://ochem.eu). A data set of 1792 toxicants was used to create models. The QSAR (Quantitative Structure-Activity Relationship) models were developed using the molecular descriptors Dragon, ISIDA, CDK, PyDescriptor, alvaDesc, and SIRMS and machine learning methods, such as Least Squares Support Vector Machine and Transformer Convolutional Neural Network. The HYBOT descriptors and Random Forest were used for a comparative QSAR investigation. Since the best predictive ability was demonstrated by the Transformer Convolutional Neural Network model, it was used to predict the toxicity of individual avermectins towards T. pyriformis. During a structural interpretation of the developed QSAR model, we determined the significant molecular transformations that increase and decrease the acute toxicity of organic compounds.

Dengue virus non-structural (NS) protein of the dengue virus serotype 5 receptor plays a critical role in the replication of the dengue virus and is also a drug target. In principle, NS-(5) receptor inhibitors can be used to treat flaviviral-associated infections. A quantitative structure–activity relationship (QSAR) analysis of anti-dengue properties and molecular docking simulation on twenty-five (25) 2-phenyl-5-[(E)-2-(thiophen-2-yl)ethenyl]-1,3,4-oxadiazole and 3-phenyl-5-[(E)-2-(thiophen-2-yl)ethenyl]-1,2,4-oxadiazole derivative was conducted in our study. Several types of molecular descriptors were generated from the geometrically optimized compounds. The multiple linear regression (MLR) method was employed for the descriptors selection that forms a model that correlates the structural function to the inhibitory action of compounds (inhibitors). The model showed acceptable validation factors (R2Pred. = 0.899, R2 = 0.951, $$Q_{{CV}}^{2} = 0.824$$ , c $${\mathrm{R}}_{\mathrm{p}}^{2}=0.815$$ , $$R_{{adj.}}^{2} = 0.939$$ ) which scaled the model criteria of acceptability. A good correlation coefficient was obtained, and the anti-dengue activity of these compounds was excellently predicted. The results of the docking simulation of the more potent compounds (ligands 15, 26, and 34) showed some significant interaction such as the formation of hydrophobic and hydrogen bonds with the DENV-2 NS-(5) receptor binding pockets and also indicated that the compounds bind favorably to the receptor. It can be predicted that compounds 15, 26, and 34 can display better inhibitory activity based on the obtained docking results. Compound 34, however, had the best binding affinity score. This investigation provides a piece of basic theoretical knowledge to enhance the binding competence of the compounds studied with the therapeutic objective. For the screening of the studied compound as anti-dengue drugs, the established model is expected to be useful in designing some more potent derivatives of oxadiazole against the dengue virus.

The aim. The aim of the study was to reveal QSAR and ascertain the possible mechanism of action via docking study in the row of tricyclic quinoline derivatives with diuretic activity. Materials and methods.Pyrrolo- and pyridoquinolinecarboxamides with proven diuretic activity were involved in the study. Molecular descriptors were calculated using HyperChem and GRAGON software, and QSAR models were built using BuildQSAR software. For receptor-oriented flexible docking, the Autodock 4.2 software package was used. Results.Multivariate linear QSAR models were built on two datasets of quinolinecarboxamides:Vol = a∙X1+ b∙X2+ c∙X3+ d, where Vol – volume of the daily produced urine in rats, Xi– molecular descriptor. QSAR analysis showed that the diuretic activity is determined by the geometric and spatial structure of molecules, logP, the energy values, RDF- and 3D-MoRSE-descriptors. Based upon internal and external validation of the models, the most informative two-parameter linear QSAR model3аwas proposed. Docking data showed the high affinity of two lead compounds to the carbonic anhydrase II. Conclusions.QSAR analysis of tricyclic quinoline derivatives revealed that the diuretic activity increases with the increase of value of logP, refractivity, and dipole moment and with the decrease of volume, surface area, and polarization of the molecules. Increase of values of such energy descriptors as bonds energy, core-core interaction, and energy of the highest occupied molecular orbital results in higher diuresis; decrease in hydration energy leads to higher diuretic activity. Based upon molecular docking calculation, the mechanism of diuretic action is proposed to be carbonic anhydrase inhibition. QSAR models and docking data are useful for in-depth study of diuretic activity of tricyclic quinolines and could be a theoretical basis for de novo-design of new diuretics

Background: Emergence of cross-resistance to current anti-malarial drugs has led to an urgent need for identification of potential compounds with novel modes of action and anti-malarial activity against the resistant strains. One of the most promising therapeutic targets of anti-malarial agents related to food vacuole of malaria parasite is haemozoin, a product formed by the parasite through haemoglobin degradation. Methods: With this in mind, this study developed two-dimensional-quantitative structure–activity relationships (QSAR) models of a series of 21 haemozoin inhibitors to explore the useful physicochemical parameters of the active compounds for estimation of anti-malarial activities. The 2D-QSAR model with good statistical quality using partial least square method was generated after removing the outliers. Results: Five two-dimensional descriptors of the training set were selected: atom count (a_ICM); adjacency and distance matrix descriptor (GCUT_SLOGP_2: the third GCUT descriptor using atomic contribution to logP); average total charge sum (h_pavgQ) in pKa prediction (pH = 7); a very low negative partial charge, including aromatic carbons which have a heteroatom-substitution in “ortho” position (PEOE_VSA-0) and molecular descriptor (rsynth: estimating the synthesizability of molecules as the fraction of heavy atoms that can be traced back to starting material fragments resulting from retrosynthetic rules), respectively. The model suggests that the anti-malarial activity of haemozoin inhibitors increases with molecules that have higher average total charge sum in pKa prediction (pH = 7). QSAR model also highlights that the descriptor using atomic contribution to logP or the distance matrix descriptor (GCUT_SLOGP_2), and structural component of the molecules, including topological descriptors does make for better anti-malarial activity. Conclusions: The model is capable of predicting the anti-malarial activities of anti-haemozoin compounds. In addition, the selected molecular descriptors in this QSAR model are helpful in designing more efficient compounds against the P. falciparum 3D7A strain., Malaria Journal, 20, art. no. 264; 2021

According to recent studies, the main Mpro protease of the SARS-CoV-2 virus, which is the most important target in the development of promising drugs for the treatment of COVID-19, is evolutionarily conservative and has not undergone significant changes compared with the main Mpro protease of the SARS-CoV virus. Many researchers note the similarity between the binding sites of the main Mpro protease of SARS-CoV and SARS-CoV-2 viruses; thus, with the spreading epidemic, further studies on inhibitors of the main Mpro protease of the SARS-CoV virus to fight COVID-19 seems logical. In the course of the study, satisfactory QSAR models are built using simplex, fractal, and HYBOT descriptors; the Partial Least Squares (PLS), Random Forest (RF), Support Vectors, Gradient Boosting (GBM) methods; and the OCHEM Internet platform ( https://ochem.eu ), in which different types of molecular descriptors and machine learning methods are implemented. The structural interpretation, which allowed us to identify molecular fragments that increase and decrease the activity of SARS-CoV inhibitors, is performed for the obtained models. The results of the structural interpretation are used for the rational molecular design of potential SARS-CoV-2 inhibitors. The resulting QSAR models are used for the virtual screening of 2087 FDA-approved drugs.

Volatile organic compounds (VOCs) pose an ever-growing threat on human health and environment. Predicting VOC affinity coefficient and consequently estimating activated carbon adsorption requires fundamental understanding of adsorbate-adsorbent interaction, shape and hydrophilicity effects. Hence, a model which expressed these three factors with molecular descriptors was investigated with Ridge and V-matrix regression methods in two cases, k-fold cross-validation and random sampling technique. The results showed, the sole interaction term and the complete model decreased the root mean square error (RMSE) of polarizability ratio by approximately 19% and 26% respectively. The V-matrix regression, reduced the average Ridge RMSE by 9 and 19% for the first and second case. Lower than 10% errors were displayed by 104 out of 155 data and only 4 data which were small molecules with very high polarity had more than 30% error. For both cases the proposed model with V-matrix regression had better or similar results compared to previous research. However, the effect of reference compounds on highly polar VOCs requires further investigation. From the VOC adsorption estimation, it was evident that affinity and adsorption errors were in the same magnitude. Hence, with accurate prediction of affinity coefficient, adsorption isotherms of any VOC can be calculated.

In this paper, a combined approach based on a k-means algorithm and statistical analysis has been applied successfully to classify 500 cytotoxic agents using 21 molecular descriptors. The k-means algorithm applied on the first two principal components split the observations into homogeneous clusters, in order to examine similarities and dissimilarities between molecules within and between clusters. Three clusters are clearly distinguished and the percentage of molecules in each cluster is 50%, 24.88%, and 25.12% for cluster 1, cluster 2, and cluster 3, respectively. Silhouette analysis is used as a cluster validation method, confirming that all the molecules are very well clustered. The silhouette indices obtained for each cluster are 0.53 for cluster 1, 0.60 for cluster 2, and 0.57 for cluster 3. Therefore, the average silhouette index obtained is 0.56. The value of the Hopkins statistic obtained is 0.922 confirming that the dataset is highly clusterable. In addition, the paragons of each cluster have been identified. The test statistic was performed to characterize each cluster by a subset of molecular descriptors. Moreover, in silico screening of pharmacological properties ADME and evaluation of drug-likeness of molecules showed that cluster 1 molecules have the best ADME profile and drug-likeness. The quantitative analysis of molecular electrostatic potential (MEP) on van der Waals surface was performed to identify the nucleophilic and electrophilic sites in the paragons molecules. Finally, molecular docking was performed for the paragon molecules on six different targets. Docking studies support the results observed in the MEP analysis showing that the favorable reactive sites of molecules are involved in strong hydrogen interactions with the functional residues of receptors.

The median effective concentration (EC50) is the concentration of a substance expected to produce a specific effect in 50% of the populations with a certain density under defined conditions. This parameter is expressed as an acute toxicity and is obtained via chemical toxicity testing. But, the laboratory work is time-consuming, expensive, and not eco-friendly. Therefore, to predict EC50 for new anionic surfactants, a quantitative structure-activity relationship (QSAR) tool was studied for modeling the EC50 of anionic surfactants on Daphnia magna based on the molecular descriptors. The best model (R2 = 0.901 and F = 118.077, p

Aims:: The aim of this letter is to explore the influence of adding hydrogen atoms to the crystallographic structures of HIV-1 protease complexes with a series of inhibitors on the performance of radial distribution function based descriptors recently introduced in chemoinformatic studies. Background:: Quite recently the successful application of molecular descriptors based on a radial distribution function to correlate it with biologically interesting properties of a ligand – enzyme complex was demonstrated. Except its predictive power, the analysis of atoms with dominant contributions to the RDFs can be used to identify relevant atoms and interactions. Since original paper was published on dataset consisting of the X-ray structures of complexes without hydrogen atoms, we wonder weather addition of light atoms can provide us new piece of information. Objective:: The primarily objective is to create the model correlating the RDF based descriptors and physicochemical properties of the HIV-1 protease complexes with inhibitors with hydrogen atoms. Then, we will compare the performance of new model with previous one, where the hydrogen atoms were discarded. Information about interactions between the enzyme and the inhibitors will be extracted from the analysis of the RDF. Methods:: The radial distribution function descriptor weighted by the number of valence shell electrons has proven to be sensitive to the changes in the structure of the enzyme and enzyme-ligand complexes. For each structure in our data set, RDF will be calculated and using multiple linear regression method the mathematical model will be designed correlating RDF based descriptors and the physicochemical properties. Statistical analysis of the atom’s contribution to the total RDF will reveal relevant interactions. Results:: The applicability of RDF based descriptor for the correlation with pKi and EC50 values is demonstrated, while simple models containing only two or three parameters are able to explain 78 and 86 % of the variance, respectively. The models with explicitly included hydrogens are of comparable quality with the previous models without hydrogens. The analysis of the atom’s dominant contributions highlighted the importance of the hydroxyl groups of the inhibitor near the Asp25 and Asp25’ residues when it is bounded to the protease. Conclusion:: Models based on the RDF weighted by the number of valence shell electrons for correlating small number of molecular descriptors and physicocehmical properties for structures with and without hydrogens are of comparable quality and both can be used for identification of relevant functional groups and interactions. Other:: Our approach can be integrated to the next generation virtual screening methods, because is fast, reliable with high predictability potential.

Organophosphates (OPs) are hazardous chemicals widely used in industry and agriculture. Distribution of their residues in nature causes serious risks to humans, animals, and plants. To reduce hazards from OPs, quantitative structure-activity relationship (QSAR) models for predicting their acute oral toxicity in rats and mice and inhibition constants concerning human acetylcholinesterase were developed according to the bioactivity data of 456 unique OPs. Based on robust, two-dimensional molecular descriptors and quantum chemical descriptors, which accurately reflect OP electronic structures and reactivities, the influences of eight machine-learning algorithms on the prediction performance of the QSAR models were explored, and consensus QSAR models were constructed. Several strict model validation indices and the results of applicability domain evaluations show that the established consensus QSAR models exhibit good robustness, practical prediction abilities, and wide application scopes. Poor correlation was observed between acute oral toxicity at the mammalian level and the inhibition constants at the molecular level, indicating that the acute toxicity of OPs cannot be evaluated only by the experimental data of enzyme inhibitory activity, their toxicokinetic characteristics must also be considered. The constructed QSAR models described herein provide rapid, theoretical assessment of the bioactivity of unstudied or unknown OPs, as well as guidance for making decisions regarding their regulation.

Materials informatics is an emerging field that allows us to predict the properties of materials and has been applied in various research and development fields, such as materials science. In particular, solubility factors such as the Hansen and Hildebrand solubility parameters (HSPs and SP, respectively) and Log P are important values for understanding the physical properties of various substances. In this study, we succeeded at establishing a solubility prediction tool using a unique machine learning method called the in-phase deep neural network (ip-DNN), which starts exclusively from the analytical input data (e.g., NMR information, refractive index, and density) to predict solubility by predicting intermediate elements, such as molecular components and molecular descriptors, in the multiple-step method. For improving the level of accuracy of the prediction, intermediate regression models were employed when performing in-phase machine learning. In addition, we developed a website dedicated to the established solubility prediction method, which is freely available at "http://dmar.riken.jp/matsolca/".

This QSAR study involved a series of benzimidazolylchalcone derivatives. It allowed us to obtain a model from the molecular descriptors and anthelminthical activity against Haemonchus contortus. The molecular descriptors were obtained by applying the methods of quantum chemistry at the B3LYP/6-31G (d) level. The statistical indicators of the model are: the coefficient of determination R2 equals 0.990, the standard deviation S equals 0.209, the Fischer coefficient F equals 153.055 and the cross-validation coefficient equals 0.990. This model has good statistical performances. The quantum descriptors of dipole moment (μD) and electronic energy are responsible of the anthelminthic activity of benzimidazolylchalcone derivatives. In addition, the dipole moment is the priority descriptor for the prediction of the antibacterial activity of the studied compounds. The Eriksson et al. acceptance criteria used for the test set is verified. The values of the pCL100theo/pCL100exp ratio of the theoretical and experimental activities for the test set aim towards the unit.

This paper presents modified versions of a recent swarm intelligence algorithm called Harris hawks optimization (HHO) via incorporating genetic operators (crossover and mutation CM) boosted by two strategies of (opposition-based learning and random opposition-based learning) to provide perfect balance between intensification and diversification and to explore efficiently the search space in order to jump out local optima. Three modified versions of HHO termed as HHOCM, OBLHHOCM and ROBLHHOCM enhance the exploitation ability of solutions and improve the diversity of the population. The core exploratory and exploitative processes of the modified versions are adapted for selecting the most important molecular descriptors ensuring high classification accuracy. The Wilcoxon rank sum test is conducted to assess the performance of the HHOCM and ROBLHHOCM algorithms. Two common datasets of chemical information are used in the evaluation process of HHOCM variants, namely Monoamine Oxidase and QSAR Biodegradation datasets. Experimental results revealed that the three modified algorithms provide competitive and superior performance in terms of finding optimal subset of molecular descriptors and maximizing classification accuracy compared to several well-established swarm intelligence algorithms including the original HHO, grey wolf optimizer, salp swarm algorithm, dragonfly algorithm, ant lion optimizer, grasshopper optimization algorithm and whale optimization algorithm.

The fumigant and topical activities exhibited by 27 plant-derived essentials oils (EOs) on adult M. domestica housefly are predicted through the Quantitative Structure-Activity Relationship (QSAR) theory. These molecular structure based calculations are performed on 253 structurally diverse compounds from the EOs, where the number of constituents in each essential oil mixture varies between 2 to 24. A large number of 86,048 non-conformational mixture descriptors are derived as linear combinations of the molecular descriptors of the EO components. Two strategies are compared for the mixture descriptor formulation, which consider or avoid the use of the chemical composition. The multivariable linear regression QSAR models of the present work are useful for fumigant and topical applications, describing predictive parallelisms for the insecticidal activity of the analysed complex mixtures.

In the present work, at first, density functional theory calculations were performed to investigate the molecular structure of the Chlorogenic, Ellagic, and Quisqualic acids by CAMB3LYP/MidiX level of theory. A detail of quantum molecular descriptors of the title compounds such as ionization potential (IP) and Electron Affinities (EA), Hardness (η), Softness (S), Electronegativity (), Electrophilic Index (), Electron Donating Power (-), Electron Accepting Power (+) and Energy Gap (Eg) have been calculated. Pharmacokinetic properties of the title compounds and their bioactivity were investigated. In the following, a molecular docking study was carried out to screen for an effective available compound that may work as a strong inhibitor for the SARS-CoV-2 main protease Mpro. The binding energy between SARS-CoV-2 main protease Mpro and title organic acids showed a good binding affinity. Therefore, the Chlorogenic, Ellagic, and Quisqualic acids can be used for potential application against the SARS-CoV-2 main protease Mpro. © 2021 by the authors.

Linear and nonlinear quantitative structure–property relationship (QSPR) models were developed based on a dataset with 65 polymer-solvent combinations. Seven quantum chemical descriptors, dipole moment, hardness, chemical potential, electrophilicity index, total energy, HOMO and LUMO orbital energies, were calculated with density functional theory at the B3LYP/6-31 G(d) level for polymers and solvents. Considering the strong correlation between intrinsic viscosity and weight, size, shape as well as topological structure of polymers and solvents, topological descriptors were also applied in this work. Meanwhile, the most appropriate polymer structure representation was investigated by considering 1–5 monomeric repeating units. The molecular descriptors were first screened by using the genetic algorithms-multiple linear regression (GA-MLR), with coefficient of determinations (r2) of 0.78 and 0.83 for the training set and the prediction set, respectively. The support vector machine model (SVM) model based on the selected descriptors subset showed a r2 value of 0.95 for the training set and 0.93 for the prediction set. All statistical results suggest that the established QSPR models have good predictability. Furthermore, a new test set obtained from the literature was used for further validation. The r2 values were 0.81 for the MLR model and 0.90 for the SVM model.

Recently, the symmetric division deg ($ SDD $) index is proven to be a potentially useful molecular descriptor in QSAR and QSPR (quantitative structure-activity and structure-property relationships) studies. And its predictive capability is better than that of some popular topological indices, such as the famous geometric-arithmetic index and the second Zagreb index. In this work, the maximum $ SDD $ indices of trees with given matching number or domination number or independence number or number of pendant vertices or segments or diameter or radius are presented. Furthermore, the corresponding extremal trees are identified.

New isomers of naphthalene-monothioureas, 3a-3c, were synthesized and characterized by using infrared (IR), 1H and 13 C nuclear magnetic resonance (NMR) and mass spectroscopies (MS). The compounds were obtained from the reaction ofo-, m-, p- toluidine with naphthyl isothiocyanate using conventional reflux and microwave-assisted irradiation methods. Microwave-assisted reaction gave higher yields with reduces the reaction time compared to the conventional heating method. Density Functional Theory (DFT) calculations with 6-31G(d, p) basic set at the B3LYP level is carried out to optimize the structure of isomers and calculate the IR, 1H and 13 C NMR spectra. The DFTB3LYP calculation of FTIR, 1H and 13C NMR spectra of 3a-3c isomers were in accordance with the experimental data. The calculation of frontier molecular orbitals was also selected to study a relationship as a molecular descriptor in antibacterial activity. The isomers were screened for their in vitro antibacterial activity by using Staphylococcus aureus bacteria. Inhibition activity was displayed for compound 3c with the value of inhibition zone 7 ± 0.34 mm at a concentration of 10 μg/mL. All compounds appeared to be in agreement with Lipinski’s rule of five assessments as they adhered to most of the rules that can be preliminarily classified as active drug-like.

Topological index sometimes called molecular descriptor is a numerical value which associates a chemical composition for correlating chemical structure with numerous physical properties, chemical reactivity, or biological activity. In this paper, we study some topological indices of boron and try to correlate the physicochemical properties such as freezing points, boiling points, melting points, infrared spectrum, electronic parameters, viscosity, and density of chemical graphs. We discuss these topological indices, and some of them are mentioned here such as Randic index, the first general Zagreb index, the general sum connectivity index, hyper-Zagreb index (HM), the atom-bond connectivity index (ABC), the geometric-arithmetic index (GA), the harmonic index (H), and the forgotten index (F).