Genetic Function Approximation Research Articles

Prediction of Lubrication Performances of Vegetable Oils by Genetic Functional Approximation Algorithm

Vegetable oils, which are considered potential lubricants, are composed of different types and proportions of fatty acids. Because of their diverse types and varying compositions, they exhibit different lubrication performances. The genetic function approximation algorithm was used to model the quantitative structure–property relationship between fatty acid structure and the wear scar diameter and friction coefficients measured by four-ball friction and wear tests. Based on the models with adjusted R2 greater than 0.9 and fatty acid compositions of vegetable oils, the wear scar diameter and friction coefficients of Xanthoceras sorbifolia bunge oil and Soybean oil as validation oil samples were predicted. The difference between the predicted and experimental values was small, indicating that the models could accurately predict the lubrication performances of vegetable oils. The lubrication performances of 14 kinds of vegetable oils were predicted by GFA-QSPR models, and the primary factors influencing their lubrication properties were studied by cluster analysis. The results show that the content of C18:1 has a positive effect on the lubrication performances of vegetable oils, while the content of C18:3 has a negative effect, and the length of the carbon chain of fatty acids significantly affects their lubrication properties.

A General Model of Impact Sensitivity for Nitrogen-Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study.

In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R2≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R2≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.

Grammar of Impact Sensitivity: An Incremental Theory.

In this paper, we report the first attempt to quantify impact sensitivity using the second-order incremental approach based on the structural features of explosives. It has been found that impact height (h50) can be expressed via a multiplicative incremental exponential form, in which the exponents are characteristic coefficients of structural increments multiplied by their numbers in the molecule. The method was developed on a large array of experimental data (450 molecules and salts) of different energetic materials, namely, nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc., while testing of the model was performed for 170 compounds. The results demonstrate a noticeable correlation with the experimental h50 values. Thus, the corresponding R2 and RMSE for the training and test sets are 0.56 (12.5 J) and 0.63 (18.8 J), respectively. In this work, we use 53 individual structural increments, but their number can be extended, and the corresponding coefficients can be refined; this allows for increasing the prediction accuracy on-the-fly. The calculation algorithm is discussed, and the corresponding examples are presented. The performed machine-based regression analysis using genetic function approximation, multiple linear regression, and artificial neural network has proven the reasonability and informativity of the proposed incremental theory. Thus, the developed approach significantly extends our understanding of the impact sensitivity phenomenon and translates it into the category of one that can be calculated by a pocket calculator.

2,4-disubstituted 6-fluoroquinolines as potent antiplasmodial agents: QSAR, homology modeling, molecular docking and ADMET studies

ObjectiveThis work was designed to study 2,4-disubstituted 6-fluoroquinolines as antiplasmodial agents by using in silico techniques, to aid in the design of novel analogs with high potency against malaria and high inhibition of Plasmodium falciparum translation elongation factor 2 (PfeEF2), a novel drug target. MethodsQuantitative structure-activity relationships (QSAR) of 2,4-disubstituted 6-fluoroquinolines were studied with the genetic function approximation technique in Material Studio software. The 3D structure of PfeEF2 was modeled in the SWISS-MODEL workspace through homology modeling. A molecular docking study of the modeled PfeEF2 and 2,4-disubstituted 6-fluoroquinolines was conducted with Autodock Vina in Pyrx software. Furthermore, the in silico pharmacokinetic properties of selected compounds were investigated. ResultsA robust, reliable and predictive QSAR model was developed that related the chemical structures of 2,4-disubstituted 6-fluoroquinolines to their antiplasmodium activities. The model had an internal squared correlation coefficient R2 of 0.921, adjusted squared correlation coefficient R2adj of 0.878, leave-one-out cross-validation coefficient Q2cv of 0.801 and predictive squared correlation coefficient R2pred of 0.901. The antiplasmodium activity of 6-fluoroquinolines was found to depend on the n5Ring, GGI9, TDB7u, TDB8u and RDF75i physicochemical properties: n5Ring, TDB8u and RDF75i were positively associated, whereas GGI9 and TDB7u were negatively associated, with the antiplasmodium activity of the compounds. Stable complexes formed between the compounds and modeled PfeEF2, with binding affinity ranging from −8.200 to −10.700 kcal/mol. Compounds 5, 11, 16, 22 and 24 had better binding affinities than quinoline-4-carboxamide (DDD107498), as well as good pharmacokinetic properties, and therefore may be better inhibitors of this novel target. ConclusionQSAR and docking studies provided insight into designing novel 2,4-disubstituted 6-fluoroquinolines with high antiplasmodial activity and good structural properties for inhibiting a novel antimalarial drug target.

Modelling of novel bornoel analogs as Influenza A Virus inhibitors through genetic function approximation, comparative molecular fields, molecular docking, and ADMET/Pharmacokinetic studies

Influenza A Virus (IAV) is a human respiratory pathogen prone to mutations and genome re-assortment leading to global pandemics. In this study, we applied the molecular modelling strategies such as, two-dimensional (2D), three-dimensional (3D)-quantitative structure–activity relationship (QSAR), and molecular docking simulation on a novel series of borneol compounds as influenza inhibitors. The best developed 2D-QSAR models, MLR (Q2 ​= ​0.8735, R2(train) ​= ​0.9096) and ANN [3-2-1] (Q2 ​= ​0.8987, R2(train) ​= ​0.9171) revealed good and acceptable statistical validation metrics for the inhibitory activity predictions. The 3D-QSAR models were generated using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), which showed CoMFA_S ​+ ​E (Q2 ​= ​0.559, R2(train) ​= ​0.939) and CoMSIA_S ​+ ​E (Q2 ​= ​0.577, R2(train) ​= ​0.941) as the best-observed models in accordance with the model acceptability standards. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrates the steric and electrostatic molecular field relationships with the inhibitory effects of the studied molecules. Moreover, the binding modes of the active ligands were studied through molecular docking simulation with the Human Hemagglutinin (HA) receptor of influenza A virus (A/Puerto Rico/8/34(H1N1)). The studied compounds revealed the formation of H-bonds, CH-bonds, and hydrophobic interactions with the active amino acid residues such as Asn 543, Asn 614, Asn 617, Leu 618, Ser 540, Lys 539, and Lys 621 in the HA binding cavity. The prediction of drug-likeness and ADMET properties of the compounds revealed their good bioavailability and pharmacokinetic profiling. This study may provide a valuable in-silico guideline for discovering novel potent influenza inhibitors.

Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants.

Persistent organic pollutants (POPs) are ubiquitous and bioaccumulative, posing potential and long-term threats to human health and the ecological environment. Quantitative structure-activity relationship (QSAR) studies play a guiding role in analyzing the toxicity and environmental fate of different organic pollutants. In the current work, five molecular descriptors are utilized to construct QSAR models for predicting the mean and maximum air half-lives of POPs, including specifically the energy of the highest occupied molecular orbital (HOMO_Energy_DMol3), a component of the dipole moment along the z-axis (Dipole_Z), fragment contribution to SAscore (SAscore_Fragments), subgraph counts (SC_3_P), and structural information content (SIC). The QSAR models were achieved through the application of three machine learning methods: partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA). The determination coefficients (R2) and relative errors (RE) for the mean air half-life of each model are 0.916 and 3.489% (PLS), 0.939 and 5.048% (MLR), 0.938 and 5.131% (GFA), respectively. Similarly, the determination coefficients (R2) and RE for the maximum air half-life of each model are 0.915 and 5.629% (PLS), 0.940 and 10.090% (MLR), 0.939 and 11.172% (GFA), respectively. Furthermore, the mechanisms that elucidate the significant factors impacting the air half-lives of POPs have been explored. The three regression models show good predictive and extrapolation abilities for POPs within the application domain.

A critical view of the QSAR model for the prediction of a new bispyrazole derivative BPYR-P as a corrosion inhibitor for 304 SS in a 1.0 M HCl solution

Using the potentiodynamic polarization (PDP) technique, a set of seven newly synthesized bispyrazole derivatives (PYR-1 to PYR-7) were examined as corrosion inhibitors for 304 stainless steel (304 SS) in 1.0 M HCl solutions at different concentrations (10−13 to 10−5 M). The experimental results were used to create a predictive model for the anticorrosion properties of another inhibitor (BPYR-P) within the same family. The model was created by relating various physicochemical descriptors of the seven bispyrazol inhibitors to their experimental inhibitory efficiency using the quantitative structure-activity relationship (QSAR) methodology along with the genetic function approximation (GFA) technique. The predicted inhibition efficiency was validated by several electrochemical techniques, including electrochemical frequency modulation (EFM), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP). The predicted QSAR values for BPYR-P showed good agreement with experimental data, establishing the reliability of the model in predicting the inhibitor's performance. The BPYR-P acts as an excellent corrosion inhibitor for 304 SS in a 1.0 M HCl solution, and its inhibition efficiency increases with the increase in concentrations. The double-layer capacitance (Cdl) decreases and the charge transfer resistance of 304 SS increases as the BPYR-P concentrations increase. The adsorption of predicted BPYR-P on a 304 SS surface displayed mixed-type inhibitor and obeyed the Langmuir adsorption isotherm. AFM, SEM-EDX, UV–visible spectra, contact angle measurement, XRD, parameters derived from quantum chemical calculations (DFT) and molecular dynamic simulations (MD) were in good agreement with the experimental data obtained.

Chemical space navigation by machine learning models for discovering selective MAO-B enzyme inhibitors for Parkinson’s disease

Monoamine Oxidase-B (MAO-B) is a key neuroprotective target that breaks neurotransmitters such as dopamine and releases highly reactive free radicals as the by-product. Its over-expression in the brain observed due to ageing and neurodegenerative diseases contributes to worsening neuronal degeneration. Being the primary enzyme for dopamine metabolism in the substantia nigra of the brain and due to the lack of efficient drug candidates, MAO-B selective, reversible inhibition is hot topic of research in Parkinson’s disease (PD). This study developed machine learning (ML) models that predict the activity of experimentally tested indole and indazole derivatives against MAO-B using linear genetic function approximation (GFA) and two non-linear support vector machine (SVM) and artificial neural network (ANN) techniques. ANN model with an R2 of 0.9704 for the training dataset, q2of 0.9436 for cross-validation and r2of 0.9025 for the test dataset were identified as the best-performing ML model with the seven significant molecular descriptors CATS2D_04_DA, CATS2D_05_DA, CATS3D_06_LL, Mor04u, Mor25m, P_VSA_v_2 and nO. The robust ML model was then employed to design novel MAO-B inhibitors with similar core scaffolds and their biological activity prediction. ANN model was further employed in the virtual screening of 4356 molecules from the ChEMBL database. Applicability domain analysis and pharmacokinetic and toxicity profiles predicted three newly designed molecules (22 N, 23 N and 24 N) and two virtually screened best ChEMBL molecules as potential drug candidates using the ANN ML model. Molecular docking studies of the best-identified compounds were performed to understand the molecular mechanism of interactions having high binding energy and selectivity with the MAO-B enzyme. The current study shortlisted 5 potential lead compounds as potent and selective MAO-B inhibitors, which could further be carried forward for in vitro and in vivo studies to discover small molecules against neurodegenerative disease.

Structural investigation of lithium borate glasses by Raman spectroscopy: Quantitative evaluation of structural units and its correlation with density

Lithium borate glass is a widely studied system, mainly due to its good ionic conductivity, thermal conductivity and thermoluminescence properties. The properties of lithium borate glass are determined by its structure, and the change of microstructure unit has great influence on its properties. In this work, the xLi2O⋅(1-x) B2O3 (0.1 ≤ x ≤ 0.48) glass was studied by Raman spectroscopy combined with peaks fitting and density analysis. Through quantitative analysis of Raman spectrum, the change in trend of each structural unit as well as the change of the tetracoordinated boron content (N4) were obtained with the increase of Li2O content. Especially, a more detailed assignment and general transformation mechanism of structural units were presented, and the common inflection points of properties (mass density, molar volume, etc.) and structural units content changes were found. The correlation between the properties (mass density, atomic density and molar volume) and the basic structural units (BØ3, BØ2O− and BØ4−) of the lithium borate glass was established by the genetic function approximation algorithm (GFA). The study on the structural units in xLi2O⋅(1-x) B2O3 (0 ≤ x ≤ 0.48) glass network and the mutual transformation mechanism between each structural unit is helpful to explore the effects on macroscopic properties as the structural units change.

QSAR Study of N-((3-Benzamido-4-oxo-3,4-Dihydroquinazolin 2-yl)methyl)-N-(Substituted) Phenyl Benzamide as Antiulcer Agents

Suppression of gastric acid secretion by use of proton pump inhibitors is an efficient way to control hyperacidity complications. An inhibitory activity of N-((3-Benzamido-4-oxo-3, 4 dihydro quinazolin -2-yl) methyl)-N-(substituted phenyl) benzamides on H+/K+-ATPase was established and reported earlier. Thus, it is significant to develop more promising agents by quantitative structure-activity relationship (QSAR) study of 37 ligands by multi-linear regression method to link the structures of molecules with inhibitory activity on H+/K+-ATPase (pIc50). QSAR model was built using genetic function approximation protocol of the software Discovery Studio Version 2.1 using training set carrying 23 compounds. The remaining 14 compounds were used as a test set. The generated model was showing satisfying statistical qualities, r2=0.84 and predicted correlation coefficient r2pred=0.88. The theoretical approach indicates that an increase in Log D, Shadow_XZ and SC 2, and reduction of Shadow_Z length causes more inhibition of enzyme by molecule.

Identification of Novel 5-Lipoxygenase-Activating Protein (FLAP) Inhibitors by an Integrated Method of Pharmacophore Virtual Screening, Docking, QSAR and ADMET Analyses

This study explored a series of reported 5-lipoxygenase-activating protein (FLAP) inhibitors to understand their structural requirements and identify potential new inhibitor scaffolds through automated unbiased procedures. Docking studies have revealed that inhibitor binding affinity can be influenced by several key binding interactions with Phe114 and Lys116 from chain B and Val21, Phe25, His28 and Lys29 from chain C in the FLAP-binding site. A ligand-based alignment three-dimensional (3D)-quantitative structure–activity relationship (QSAR) was adopted, resulting in a robust model with a statistically significant noncross-validated coefficient ([Formula: see text]), a cross-validated correlation coefficient ([Formula: see text]) and a predictive squared correlation coefficient ([Formula: see text]). Overall, the analysis revealed the important electrostatic and steric attributes responsible for the FLAP inhibitory activity, which appeared to correlate well with the docking results. In addition, two statistically significant two-dimensional (2D)-QSAR models ([Formula: see text], [Formula: see text] and [Formula: see text], [Formula: see text]) were developed by a genetic function approximation (GFA). HypoGen 1, a proposed pharmacophore model, was used for database mining to identify potential new FLAP inhibitors. The bioactivity of the retrieved hits was then evaluated in silico based on the validated QSAR models, followed by pharmacokinetics and toxicity predictions.

Computational modelling of some phenolic diterpenoid compounds as anti-influenza A virus agents

Influenza A virus (IAV) infection is a contagious respiratory disease that causes many deaths due to the advent of drug-resistant strains in recent times. This led to the quest for the in-silico identification of potential hit scaffolds as anti-influenza A agents. In this research, two quantitative structure-activity relationships (QSAR) modelling approaches were performed to relate the molecular descriptors of some phenolic diterpenoids based on their 2D and 3D structural representations with their anti-IAV activities. Subsequently, molecular docking simulation and ADMET evaluations of the compounds were performed to virtually screen and identify the best hits accordingly. The genetic function approximation (GFA) based linear and non-linear regression such as multiple linear regression (MLR) and artificial neural network (ANN) regression models were built in the 2D-QSAR modelling, and the results showed GFA-MLR (R2train = 0.9102, Q2 = 0.8701) and GFA-ANN (R2train=0.9215, Q2=0.9216) models for predicting the anti-IAV activities of the compounds which have passed the global criteria of accepting QSAR models. The 3D-QSAR modelling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), and the results revealed CoMFA_ES (R2train = 0.948, Q2 = 0.590) and CoMSIA_EDH (R2train = 0.980, Q2 = 0.754) models for reliable predictions of anti-IAV activities. The compounds were also virtually screened based on their binding scores through molecular docking with an active site of human hemagglutinin (HA) target which confirms their resilient potency. Furthermore, the drug-likeness and ADMET predictions of the compounds showed the non-violation of Lipinski's rule and good ADMET profiles as part of the rational strategy for future in-silico drug design and discovery. The outcome of this research provides theoretical support to affirm the relevance of totarol as a promising scaffold and set a route for the in-silico design of new diterpenoid inhibitors with improved potency.

Ligand-based drug design, molecular docking and pharmacokinetic studies of some series of 1,4 - dihydropyridines derivatives as human intestinal maltase-glucoamylase inhibitor

Quantitative Structure Activity Relationship (QSAR) was employed to predict the inhibitory activities of some series of 1,4–dihydropyridines derivatives as potent C-terminal human intestinal maltase-glucoamylase inhibitor (MGAM-C). The Density Functional Theory utilizing B3LYP/6–31G* as the basis set was employed to optimize the chemical structure of 1,4–dihydropyridines derivatives. four models were generated using Genetic Function Approximation with model three having internal validation parameters of R 2 (trng set) = 0.899, R 2 adj = 0.869, Q 2 cv = 0.769, LOF = 0.0129 selected as the best since it has the highest external validation parameter of R 2 (test set) = 0.885. Five potent compounds designed using the ligand-based approach were found to be more enhanced than the template. furthermore, binding interactions of the designed compounds within the active site of (MGAM-C) showed a fascinating MolDock scores with good pharmacokinetic profiles. This study, provide a useful information for the design of new α-glucosidase inhibitors.

Computational prediction of Lee retention indices of polycyclic aromatic hydrocarbons by using machine learning.

Given the difficult of experimental determination, quantitative structure-property relationship (QSPR) and deep learning (DL) provide an important tool to predict physicochemical property of chemical compounds. In this paper, partial least squares (PLS), genetic function approximation (GFA), and deep neural network (DNN) were used to predict the Lee retention index (Lee-RI) of PAHs in SE-52 and DB-5 stationary phases. Four molecular descriptors, molecular weight (MW), quantitative estimate of drug-likeness (QED), atomic charge weighted negative surface area (Jurs_PNSA_3), and relative negative charge (Jurs_RNCG) were selected to construct regression models based on genetic algorithm. For SE-52, PLS model showed best prediction power, followed by DNN and GFA. The relative error (RE), root mean square error (RMSE), and regression coefficient (R2 ) of best PLS regression model are 1.228%, 5.407, and 0.980. For DB-5, DNN model showed best prediction power, followed by GFA and PLS. The RE, RMSE and R2 of best DNN regression model for DB-5-1 and DB-5-2 are 1.058%, 4.325%, 0.976%, 0.821%, 3.795%, and 0.970%, respectively. The three regression models not only show good predictive ability, but also highlight the stability and ductility of the models.

In silico prediction of boiling point, octanol-water partition coefficient, and retention time index of polycyclic aromatic hydrocarbons through machine learning.

Polycyclic aromatic hydrocarbons (PAHs), a special class of persistent organic pollutants (POPs) with two or more aromatic rings, have received extensive attention owing to their carcinogenic, mutagenic, and teratogenic effects. Quantitative structure-property relationship (QSPR) is powerful chemometric method to correlate structural descriptors of PAHs with their physicochemical properties. In this manuscript, a QSPR study of PAHs was performed to predict their boiling point (bp), octanol-water partition coefficient (LogKow ), and retention time index (RI). In addition to traditional molecular descriptors, structural fingerprints play an important role in the correlation of the above properties. Three regression methods, partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA), were used to establish QSPR models for each property of PAHs. The correlation coefficient (R2 test ) and root mean square error (RMSE) of best model were 0.980 and 24.39% (PLS), 0.979 and 35.80% (GFA), 0.926 and 22.90% (MLR) for bp, LogKow, and RI, respectively. The model proposed here can be used to estimate physicochemical properties and inform toxicity prediction of environmental chemicals.

Prediction of aquatic toxicity of energetic materials using genetic function approximation

• Prediction of aquatic toxicity of nitrogen-rich energetic materials is reported. • A sophisticated regression method, namely, genetic function approximation was used. • The obtained QSPR models provide good correlation ( R 2 = 0.81-0.82) for various exposure times. • The models were tested for various families of energetic materials and experimental conditions. The first attempt to use genetic function approximation (GFA) for prediction of aquatic toxicity of soluble energetic materials is reported in this paper. The prediction is based on the estimation of the luminescent bacteria Aliivibrio fischeri inhibition in water according to the recently reported experimental results. Thus, two quantitative structure-activity relationship (QSAR) models for 15 and 30 min exposure were obtained, which include five and six essential descriptors, respectively. Most of them are so-called “fast descriptors” assuming there is no need for quantum-chemical calculations. The rest descriptors are obtained in terms of semi-empirical approach allowing the prediction to be rapidly complete. The developed QSAR models provide relatively high correlation coefficients, namely, R 2 = 0.81 and 0.82 for 15 min and 30 min datasets, respectively. The experimental datasets included a number of values, which were presented ambiguously (< or > than certain values). Thus, these have not been included (13 for 15 min and 10 for 30 min datasets) in the training sets and used them as the corresponding test sets. As a result, the developed models accurately indicate what exactly the higher and lower values should be applied instead of ones presented with ambiguity. Thus, the results may be useful for predicting the aquatic toxicity of new nitrogen-rich energetic materials, both molecular and ionic, bearing nitro, nitramino, azido groups and other commonly used explosophores.

A combined 2-D and 3-D QSAR modeling, molecular docking study, design, and pharmacokinetic profiling of some arylimidamide-azole hybrids as superior L. donovani inhibitors

BackgroundLeishmaniasis is one of the neglected tropical diseases which is prevalent in the tropical regions of the world most especially in Africa. It is caused by the Leishmania species and transmitted to humans majorly through the bite of the female sandfly. This study was carried out in support of the continuous search for new drug molecules effective enough for the treatment of leishmaniasis, and which have very limited side effects. This study was focused on a combined 2-D and 3-D QSAR modeling of thirty-six arylimidamide-azole hybrids, molecular docking study, design, and pharmacokinetic analysis of some selected and newly designed arylimidamide-azole analogs. The density functional theory (DFT) with B3LYP and 6-31G** basis set was employed for the geometry optimization of the various compounds. Genetic function approximation (GFA) and multi-linear regression (MLR) approaches were used for the 2-D QSAR model building, while the fractional factorial design (FFD) and uninformative variable elimination-partial least square (UVEPLS) were employed for building the 3-D QSAR model. Pyridoxal kinase (PdxK) receptor (PDB: 6K91) was the target protein of interest in this study.ResultsThe built 2-D and 3-D QSAR models were found to satisfy the requirement of both internal and external validation tests as follows: 2-D QSAR; R2 = 0.9614, R2adj = 0.9513, Q2cv = 0.9350, R2test = 0.6766 and cRp2 = 0.8779, and for 3-D QSAR (UVEPLS at PC = 5); R2 = 0.9839, Q2LOO = 0.7539 and Q2LTO = 0.7367. The CoMFA steric and electrostatic field contributions were 68.96% and 31.04%, respectively. All the designed analogs showed higher predicted activities than that of the template (36). Also, the new compounds showed higher binding affinities (MolDock scores) than that of the reference drug pentamidine (− 141.793 kcal/mol), with 36e showing the highest negative MolDock score of − 208.595 kcal/mol. Additionally, these newly designed compounds were said to be orally bioavailable (excluding 36f and 36g that violated 2 of the Lipinski’s provisions), with perfect intestinal absorption, less difficult to synthesize, AMES toxicity free, and showed strong interactions with the target.ConclusionsThe newly designed compounds especially 36e have shown a marked pharmacological improvement over the template molecule and are therefore recommended for further practical evaluation as superior pyridoxal kinase inhibitors.

Molecular Mechanics-Based Quantitative Structure-Activity Relationship Study on the Inhibitory Activity of Some Schiff Bases against Escherichia coli

Due to their high inhibitory action against Escherichia coli (E. coli), the rise of multidrug-resistant strains of the bacteria necessitates the testing and development of a new set of Schiff bases as anti-E. coli agents worldwide. In this study, the Genetic function approximation (GFA) Quantitative structure-activity relationship (QSAR) analyzes selected Schiff bases with anti-E. coli activity. This was done using different molecular descriptors and Hansch's approach, which results in the production of one statistically significant hepta parameter model as the strongest model with a squared correlation coefficient (R2) = 0.828, adjusted squared correlation coefficient (R2adj) = 0.775, cross-validated correlation coefficient (Q2) = 0.691, Difference between R2 and Q2, Q2 (R2 - Q2) = 0.137, external prediction (R2pred.) = 0.751 and lack of fit (LOF) of 0.067 value were selected as the best model based on its sound statistical parameters. The development model demonstrated the predominance of the descriptors Minimum H E State (Hmin) and Valence path order 6 (VP-6) in influencing the observed anti-E. coli activity of Schiff bases. Insilico techniques can certainly provide a quick, inexpensive and safe quantitative risk assessment for this class of compounds. It is envisaged that the QSAR results discovered in this work will provide crucial structural insights towards the design of effective anti-E. coli drugs based on Schiff bases

Quantitative structure–activity relationship modeling of hydroxylated polychlorinated biphenyls as constitutive androstane receptor agonists

Hydroxylated polychlorinated biphenyls (OH-PCBs), a series of toxic chemical compounds produced via biotic and abiotic transformation of polychlorinated biphenyls (PCBs), are known to cause endocrine disruption by interacting inappropriately with human nuclear receptors. Due to occurrence of high numbers of inactive OH-PCB congeners recorded in many experimental toxicity studies, it is pertinent to develop rapid and inexpensive QSAR models that can reliably predict the activities of OH-PCB congeners prior to experimental testing. Using a combination of genetic function approximation and multiple linear regression methods, a local QSAR model, consisting of six 2D descriptors (MATS1s, VE3_DzZ, VE1_Dzp, SpMin8_Bhv, SpMax5_Bhi, topoRadius) and two 3D descriptors (RDF95u, RDF45m), was developed from a training set of 44 OH-PCBs. Statistical parameters for fitting ( $${R}^{2}$$ = 0.8902, $${R}_{adj}^{2}$$ = 0.8651, s = 0.2840), cross-validation ( $${Q}_{LOO}^{2}$$ = 0.8201, $${RMSE}_{CV}$$ = 0.3242), and Y-randomization ( $${cR}_{p}^{2}$$ = 0.8019) obtained for the developed QSAR model indicate that the model is reliable, robust, and provides good fit to the data in the training set. The results of external validation carried out on 20 OH-PCBs in the test set also indicate that the developed QSAR model possessed good external predictivity and can be used to predict the agonistic activities of untested OH-PCB congeners to constitutive androstane receptor.

Friction sensitivity of nitramine energetic materials: a prediction based on genetic function approximation

In this paper, we report a quantitative structure-property relationship (QSPR) model development for friction sensitivity prediction of nitramine energetic materials. The model is obtained by means of a sophisticated method, namely, genetic function approximation and consists of 10 descriptors. As a training set, we have applied 80 nitramine energetic materials of a few families, both molecular and salt-like. The corresponding test set included 30 compounds. As a result, we have obtained a good correlation with R2 = 0.90. Only two descriptors, the heat of formation and quadrupole components, need semi-empirical quantum-chemical calculations, while the rest ones are so-called “fast descriptors” meaning the relatively short time of the prediction. Using the obtained QSPR model along with our recent method for fast crude estimation of the detonation properties based on empirical formulas we have modified a few energetic salts in order to obtain materials with better friction sensitivity and detonation performance. This was mainly done by changing the constituting ions: thus, 10 nitramine energetic materials with improved characteristics were proposed.