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Background: Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a vital role in predicting biological effect. Objective: Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter Gamma Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA receptor. Method: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular descriptors. Three QSAR models are built using gradient boosting regression tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand-receptor interaction characteristics. Results: The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and 0.03 respectively. Conclusion : We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to build the QSAR model more accurately.

Clathrin-mediated endocytosis (CME) is a normal biological process where cellular contents are transported into the cells. However, this process is often hijacked by different viruses to enter host cells and cause infections. Recently, two proteins that regulate CME – AAK1 and GAK – have been proposed as potential therapeutic targets for designing broad-spectrum antiviral drugs. In this work, we curated two compound datasets containing 83 AAK1 inhibitors and 196 GAK inhibitors each. Subsequently, machine learning methods, namely Random Forest, Elastic Net and Sequential Minimal Optimization, were used to construct Quantitative Structure Activity Relationship (QSAR) models to predict small molecule inhibitors of AAK1 and GAK. To ensure predictivity, these models were evaluated by using Leave-One-Out (LOO) cross validation and with an external test set. In all cases, our QSAR models achieved a q2LOO in range of 0.64 to 0.84 (Root Mean Squared Error; RMSE = 0.41 to 0.52) and a q2ext in range of 0.57 to 0.92 (RMSE = 0.36 to 0.61). Besides, our QSAR models were evaluated by using additional QSAR performance metrics and y-randomization test. Finally, by using a concensus scoring approach, nine chemical compounds from the Drugbank compound library were predicted as AAK1/GAK dual-target inhibitors. The electrostatic potential maps for the nine compounds were generated and compared against two known dual-target inhibitors, sunitinib and baricitinib. Our work provides the rationale to validate these nine compounds experimentally against the protein targets AAK1 and GAK.

Hua Loo-Keng’s problem is solved for primes, four of which have binary expansions of a special form, whilst the fifth satisfies the inequality , where . Bibliography: 13 titles.

A quantitative structure-activity relationship (QSAR) study was performed to develop a model on a series of 3, 5-dimethylpyrazole containing furan moiety derivatives which exhibited considerable inhibitory activity against PDE4B. The obtained model has correlation coefficient (r) of 0.934, squared correlation coefficient (r2) of 0.872, and leave-one-out (LOO) cross-validation coefficient (Q2) value of 0.733. The predictive power of the developed model was confirmed by the external validation which has (r2) value of 0.812. These parameters confirm the stability and robustness of the model to predict the activity of a new designed set of 3,5-dimethyl-pyrazole derivatives (I-XV), results indicated that the compound III, V, XIII, and XV showed the strongest inhibition activity (IC50 = 0.2813, 0.5814, 0.6929, 0.6125μM, respectively) against PDE4B compared to the reference rolipram with (IC50=1.9μM). Molecular docking was performed on a new designed compound with PDE4B protein (3o0j). Docking results showed that compounds (X and IX) have high docking affinity of -36.2037 and -33.2888 kcal/mol respectively. Keywords: QSAR, molecular docking, pyrazole derivatives, PDE4 inhibitors, anti-inflammatory.

Background: In this study, we used a hierarchical approach to develop quantitative structure-activity relationship (QSAR) models for modeling physico-chemical properties of quinolone derivatives. Objective: The relationship between some of the molecular descriptors with physic-chemical properties such as refractive index (n), polarizability (α) and HOMO-LUMO energy gap (ΔEH-L) was represented. Materials and Methods: Quantum mechanical calculations using abinitio method at the #HF/6- 31++G** level were carried out to obtain the optimized geometry and then, the comprehensive set of molecular descriptors was computed by using the Dragon software. Genetic algorithm using multiple linear regression (GA-MLR) with backward method by SPSS software were utilized to construct QSAR models. Results: The analytical powers of the established theoretical models were discussed using leaveone- out (LOO) cross-validation technique. A multi-parametric equation containing maximum three descriptors with suitable statistical qualities was obtained for predicting the studied properties. Conclusion: The QSPR analysis for the prediction of the refractive index, the polarizability and the HOMO-LUMO energy gap of 40 quinolone derivatives using GA-MLR method was performed. The achieved results showed that the best model for predicting the refractive index, the polarizability and the HOMO-LUMO energy gap contains maximum three descriptors. MLR analysis, using genetic algorithms as suitable descriptors selection method showed that the three selected descriptors play a vital role in the prediction of physicochemical properties of quinolone derivatives. It can be noted that the best descriptors in the final obtained models can be used to design and screen new drugs.

A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R2) = 0.8884, cross-validated R2 (Q2cv) = 0.8317 and highest external validated R2 (R2pred) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC50 = 8.9545).

A comprehensive study on anthracycline derivatives was done. A quantitative structure–activity relationship (QSAR) study on the half-maximal inhibitory concentration (IC50) of these analogs was developed. These antitumor compounds are used as topoisomerase II enzyme inhibitors. Genetic algorithm (GA) was applied for feature extraction. Multiple linear regression (MLR) was established based on the GA. High stability and robustness of the model were evaluated by leave-one-out cross-validation (LOO-CV), Y-randomization, and external test set (R2 = 0.879, Q LOO 2 = 0.857, RMSE = 0.148, R max 2 = 0.224, F = 45.4, PRESS = 0.541). This model was generalized to 29 analogs with the quantitative or qualitative in vitro observations for their pIC50 to be calculated. Good agreement between experimental observations and calculated pIC50, indicated that the model was reliable. This result also showed that probably the drug structural-specific is preferred to the cancer cell line-specific in such analogs. Furthermore, the developed model was generalized to 49 other analogs to select potent drug candidates. To do so, four criteria were used simultaneously including (i) effective inhibitory range, (ii) leverage value of structural similarity, (iii) GATS8v value as an important descriptor, and (iv) substituent effect. This approach resulted in the discrimination of 11 candidates.

Background QSAR modelling was performed on thirty-five (35) newly discovered compounds of N-(2-phenoxy) ethyl imidazo[1,2-a] pyridine-3-carboxamide (IPA) to predict their biological activities against Mycobacterium tuberculosis (MTB-H37Rv strain) by using some numerical data derived from structural and chemical features (descriptors) of the compounds. Results At first, the structure of the compounds was accurately drawn and optimized using the Spartan 14 software at DFT level of theory with B3LYP/6-31G** basis set in a vacuum. The diverse chemometric descriptors were computed from the optimized structures using the PaDEL descriptors software, and the division of the dataset into training and test sets was done based on Kennard-Stone’s algorithm. Five (5) models were generated from the training set using genetic function approximation, and model 1 was chosen as the best due to its robust internal and external validation metrics (R2train = 0.8563, R2adjusted = 0.8185, PRESS = 3.5724, average $$ {\overline{R}}_m^2 $$ R ¯ m 2 (LOO-train) = 0.6751, Q2cv = 0.7534, $$ {R}_{\mathrm{pred}}^2= $$ R pred 2 = 0.7543, R2test = 0.6993) which passed the model criteria of acceptability. 6-Bromo-N-(2-(4-bromophenoxy) ethyl)-2-ethylimidazo[1,2-a] pyridine-3-carboxamide (compound 13) was used as the structural template for the in silico design due to its high pMIC, and it is within the model’s chemical space. Conclusion Based on the information obtained from model 1, six (6) designed compounds with higher anti-tubercular activity were obtained. Furthermore, the ADME and drug-likeness prediction of the designed molecules showed good pharmacokinetic properties which indicate the application prospect of these compounds as novel MTB-H37Rv inhibitors. This research could help the medicinal chemists and pharmaceutical practitioners in future designing and development of more potent drug candidates. Graphical abstract

Background Quantitative structure-activity relationships (QSAR) is a technique that is used to produce a model that connects biological activities of compounds to their chemical structures, and molecular docking is a technique that reveals the binding mode and interactions between a drug and its target enzyme. These techniques have been successfully applied in the design and development of many drug candidates and herein were employed to build a model that could help in the development of more potent antimalaria drugs. Results Descriptors of the compounds were calculated using the PaDEL-Descriptor software, and Genetic Function Algorithm (GFA) was used to select descriptors and build the model. A robust and reliable model was generated and validated to have internal and external squared correlation coefficient (R2) of 0.9622 and 0.8191, respectively, adjusted squared correlation coefficient (Radj) of 0.9471, and leave-one-out (LOO) cross-validation coefficient (Q2cv) of 0.9223. The model revealed that the antiplasmodial activities of 1,2,4,5-tetraoxane-8-aminoquinoline hybrids depend on MATS3m, GATS8p, GATS8i, and RDF50s descriptors. MATS3m, GATS8i, and RDF50s influenced the antiplasmodial activities of the compounds positively while GATS8p negatively with the greatest influence. The docking result shows strong interactions between 1,2,4,5-tetraoxane-8-aminoquinoline hybrids and Plasmodium falciparum lactate dehydrogenase (pfLDH) with binding affinities ranging from − 6.3 to − 10.9 kcal/mol which were better than that of chloroquine (− 6.1 kcal/mol), suggesting that these compounds could be better inhibitors of pfLDH than chloroquine. Conclusion The results of this study could serve as a model for designing new potent 1,2,4,5-tetraoxane-8-aminoquinolines with better antiplasmodial activities for the development of highly active antimalaria drugs.

Background: Phosphoinositide-Dependent Kinase-1 (PDK1) is a serine/threonine kinase, which belongs to AGC kinase family required by cancer cells. Methods: harmacophoric space of 86 PDK1 inhibitors using six diverse sets of inhibitors was explored to identify high-quality pharmacophores. The best combination of pharmacophoric models and physicochemical descriptors was selected by genetic algorithm-based QSAR analysis that can elucidate the variation of bioactivity within the training inhibitors. Two successful orthogonal pharmacophores emerged in the optimum QSAR equation (r2 69 = 0.90, r2 LOO= 0.86, F= 51.92, r2 PRESS against 17 test inhibitors = 0.79). Receiver Operating Characteristic (ROC) curve analyses were used to estimate the QSAR-selected pharmacophores. Results: 5 out of 11 compounds tested had shown potential intracellular PDK1 inhibition with the highest inhibition percent for compounds 92 and 93 as follows; 90 and 92% PDK1 inhibition, respectively. Conclusion: PDK1 inhibitors are potential anticancer agents that can be discovered by combination method of ligand based design with QSAR and ROC analysis.

In this paper, we report the use of a mixture of radial distribution functions (RDFs) and molecular docking descriptors (MDDs), as a new group of descriptors, to construct a predictive quantitative structure-activity relationship (QSAR) model. The performance of the proposed mixed descriptors as the independent variables was checked with QSAR modeling of the anti-cancer activities of a series of 4-anilinoquinazoline analogs as the potent epidermal growth factor receptor (EGFR) inhibitors. The RDF descriptors were calculated using the available software. The docking descriptors were extracted by docking the understudied compounds into the active site of the protein with the PDB Code of 1M17 using molecular docking software. The stepwise linear regression was used to select the most important descriptors. The selected relevant descriptors were used as the inputs in the Bayesian regularization-artificial neural network (BR-ANN) as the QSAR model. The data set was randomly divided into training (35 compounds) and external test (8 compounds) sets. The mean square error (MSE) of the training set was applied for the selection of the optimal BR-ANN model. The validation of the proposed BR-ANN model was accomplished by the prediction of pIC50 of compounds in the external test set and all molecules through the leave-one-out (LOO) technique. The results obtained confirmed the acceptable accuracy of the model ( $$ {R}_{\mathrm{test}}^2=0.90 $$ and $$ \kern0.50em {R}_{\mathrm{LOO}}^2=0.79 $$ ).

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In the present in-silico study, various computational techniques were applied to determine potent compounds against TRAP1 kinase. The pharmacophore hypothesis DHHRR_1 consists of important features required for activity. The 3D QSAR study showed a statistically significant model with R2 = 0.96 and Q2 = 0.57. Leave one out (LOO) cross-validation (R2 CV = 0.58) was used to validate the QSAR model. The molecular docking study showed maximum XP docking scores (−11.265, −10.532, −10.422, −10.827, −10.753 kcal/mol) for potent pyrazole analogs (42, 46, 49, 56, 43), respectively, with significant interactions with amino acid residues (ASP 594, CYS 532, PHE 583, SER 536) against TRAP1 kinase receptors (PDB ID: 5Y3N). Furthermore, the docking results were validated using the 100 ns MD simulations performed for the selected five docked complexes. The selected inhibitors showed relatively higher binding affinities than the TRAP1 inhibitor molecules present in the literature. The ZINC database was used for a virtual screening study that screened ZINC05297837, ZINC05434822, and ZINC72286418, which showed similar binding interactions to those shown by potent ligands. Absorption, distribution, metabolism, and excretion (ADME) analysis showed noticeable results. The results of the study may be helpful for the further development of potent TRAP1 inhibitors

Insilico modelling studies was executed on twenty-four (24) novel C14-urea-tetrandrine compounds as inhibitors of prostate cancer (PC3) cell line. The molecular structure of each compound was correctly drawn using ChemDraw software, then optimized using Density Functional Theory (DFT/B3LYP/6-31G*) at ground state with Spartan 14 V1.1.4 software. Accordingly, the optimized structures were numerically represented by computing diverse molecular descriptors using PaDEL calculator. The entire data set results were divided into training and test set. A multi-linear regression model based on genetic function approximation in selecting statistically significant descriptors was built from the training set. The resultant QSAR model (R2train = 0.8075, Q2LOO = 0.6866, R2test = 0.6147, cRp2 = 0.7397) was adequately validated using the leave-one-out (LOO) cross-validation method, MLR Y-randomization test, bias-variance estimation (bootstrapping), and it was accepted due to its statistical significance based on threshold values of accepting QSAR model globally. Compound 1 and 11 as the best inhibitors were docked with B-cell lymphoma 2 (Bcl-2) crystal structure so as to explore the kind of interactions in each stable complex formed. The results revealed binding scores of −8.7 kcal/mol for the ligand (compound 1) and −9.3 kcal/mol for the ligand (compound 11) which is the highest. It was observed also that both inhibitors made hydrophobic and hydrogen bond interaction with the amino acid residue of B-cell lymphoma 2 (Bcl-2) protein which control cell death in prostate cancer. The present findings could be useful in designing and synthesizing new C14-urea-tetrandrine with better inhibitory potentials against prostate (PC3) cell line. Keywords: QSAR, Model, Molecular descriptors, Docking, Tetrandrine, Prostate cancer

The phencyclidine (PCP) and their analogues have been reported to exhibit inhibitory activities toward the N-methyl-D-aspartate receptor (NMDAR). To discover the QSAR between structure of PCP derivatives and Ki activities we have used density functional theory (DFT) to generate quantum descriptors, multiple regression linear (MLR) method was applied to establish QSAR model, and an artificial neural network (ANN), considering the relevant descriptors obtained with the MLR method is explored, a correlation coefficient of RANN = 0.912 was obtained with 6-4-1 ANN model. This model is tested by using a cross-validation method with the LOO procedure (RCV = 0.841). To study the configuration impact on activity, we proceed to the Molecular Docking of four configurations, two configurations of compound have (Ki = 502 nM) and two configurations of compound have (Ki = 1200nM). The phenyl group, when placed in an equatorial position in cis9e, a configuration of the less active compound, does not form π-sigma interaction. The superimposition of this configuration with trans7e reveals that the phenyl group of cis9e configuration is shifted from the binding site compared to trans7e which forms an interaction π-sigma throughout its phenyl group with ARG B: 894. So, we could claim that the cis9e is the configuration adopted by compound having (Ki = 502 nM).

A quantitative structure–activity relationship (QSAR) was carried out to analyze inhibitory activity of 35 compounds, new polyamine-sensitive inhibitors of the NMDA receptor, using multiple linear regression (MLR), artificial neural networks (NN), and the molecular descriptors were calculated using DFT method. This study shows that the compounds' activity correlates reasonably well with six selected descriptors by MLR method. The correlation coefficients calculated by MLR and after that by NN, R =0.878 and R =0.978 respectively, are relatively kind to evaluate the proposed quantitative model, and to predict activity for new polyamine-sensitive inhibitors of the NMDA receptor. The test of the performance of the NN model, using a cross-validation method with a leave-one-out procedure (LOO) shows that the predictive power of this model is relevant (R=0.966). The constitutional molecular descriptors (nN and nHBD) have the most significant impact in the formulation of the QSAR model. The molecular docking investigations exploring the influence of the structural differences in the interaction potency demonstrate that the number of N atoms expressed by multiple hydrogen bonds helps the ligand to be fixed to NR2B subtype of NMDA receptor.

Objectives: This study is aimed at generating a statistically valid model using molecular descriptors and physicochemical parameters on sulfur-containing shikonin oxime derivatives and to have better understanding of binding affinity of these compounds as well as their various interactions with the target receptor so as to pave way for the designing of highly potent anti-colon cancer drug. Materials and methods: Quantitative structure–activity relationship and molecular docking studies were carried out on 40 dataset of sulfur containing shikonin oxime derivatives. Density functional theory were employed in optimizing the molecule with B3LYP/6-31G* as basis set, while descriptor selection and generation of multiple linear regression model that correlates the structural feature of the compounds to their experimental activities was done by employing Genetic Function Algorithm (GFA). The softwares that was used in this study include Chemdraw, Spartan software, Padel, Material studio, Pyrex, Vina wizard, Autoduck, as well as Discovery studio. Results: Among the models generated, the one with best statistical significance was selected and it has R2 value of 0.92, adjusted R2 value of 0.89, and leave one out (LOO) cross validation coefficient (Q2) value of 0.85. To confirm the predictive capacity of the selected model, external validation was carried out using the test set compounds and it has the R2ext value of 0.88. Molecular docking study revealed that all the compounds have favorable binding affinity to the target receptor, compound number 23 has the highest binding affinity of −8.3 kcal/mol and favorable number of various interactions. Conclusion: Based on the parameters of internal and external validation, the robustness, stability and predictive ability of the selected model was verified, compounds number 23 with highest binding affinity and favorable number of various interactions can be used as lead compound in the feature design of highly potent anti-colon cancer drug. Keywords: Shikonin oxime, Quantitative structure–activity relationship, Molecular docking, Genetic function algorithm, Density functional theory

Background: PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding. Methods: Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

Aim and Objective: Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure–Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors. Materials and Methods: A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities. Results: The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263. Conclusion: The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.

The present work has focused on the application of the inverse-QSAR/QSPR problem for generating new structures of pesticides; this is in view of its extremely important and widespread use in several areas, particularly the agricultural field. For this reason, we implemented a methodology containing nine detailed successive steps that include a quantitative structure-activity/property relationship (QSAR/QSPR) study performed to develop a model that relates the structures of 190 pesticides compounds to their n-octanol-water partition coefficients (logkow). We used the unique atomic signatures which represent the structures and acts as independent variables while the property (logkow) as the dependent variable. The model was constructed using 130 molecules as training set, and predictive ability tested using 60 compounds. Modeling of logkow of these compounds as a function of the signatures descriptors was established by multiple linear regression (MLR) using (LOO) cross-validation. As a result, a QSAR/QSPR equation with 14 atomic signatures was hereby obtained with a R2 =0.659273, Q2 =0.65617 and RMSEtraining= 0.930192, s=1.37297 for the training set and in leave-one-out (LOO) cross-validation experiment set value, q2 =0.605676, RMSELOO= 1.0936 respectively. In addition to all of the above, new structures have been generated for a range of pesticides that can be included as future search topics.

The compound 4-hydroxy-α-tetralone (1) is major bioactive secondary metabolite of genus Ammannia (Family- Lythraceae). The compound 1 and its various derivatives are reported to possess anti-tubercular, anti-diabetic, anti-leishmanial and bioenhancing activities. A quantitative structure activity relationship (QSAR) model for predicting anti-inflammatory activity of 4-hydroxy-α-tetralone derivatives against the tumour necrosis factor (TNF)-α, a pro-inflammatory cytokine was developed by non-linear model using artificial neural network (ANN). The regression coefficient (r2) and the leave-one-out cross-validation regression coefficient (LOO rCV2) of the QSAR model were 0.6976 and 0.4016, respectively, while the regression coefficient (r2) for the external set of experimental compounds was 0.835. The 4-hydroxy-α-tetralone virtual derivatives, which showed significant inhibition of TNF-α were subjected to docking and in-silico absorption, distribution, metabolism and excretion (ADME) studies and the results showed similar binding affinity and bioavailability in compliance with the standard drug, Diclofenac. Finally, in order to validate the developed QSAR model, the most and least active virtual derivatives were semi-synthesized, characterized on the basis of their 1H and 13C NMR spectroscopic data and in vitro tested for concentration dependent inhibition of TNF-α. The experimental results obtained, agreed well with the predicted values.

The antioxidant activity of gentisic (GA) and α-resorcylic (α-RA) acids was investigated by considering their molecular structures in various oxidative environments, including DPPH· scavenging assay, stripped olive and soybean oils, and the corresponding oil-in-water emulsions. The mechanism of action in the oils was evaluated in the presence of different concentrations of the antioxidants at 60 °C, using the kinetic parameters the stabilization factor (F), the oxidation rate ratio (ORR), the activity (A), and the average rate of antioxidant consumption ($$\overline{r}_{{{\text{AH}}}}$$ r ¯ AH ). GA was significantly more potent antioxidant than α-RA in all the environments. Although the less polar α-RA showed better activity in the emulsions rather than in the bulk oils, GA with an ortho-hydroxy structure had higher capacity to scavenge DPPH·, and LOO· in the oils and emulsions. The lower performance of α-RA was attributed to its participation in side reactions of chain initiation (AH + LOOH → A· + L· + H2O) and propagation (A· + LH → AH + L·) as competed with the main chain termination reaction (LOO· + AH → LOOH + A·).

In silico molecular dynamics (MD) using crystallographic and NMR data was used to simulate the effects of the protonation state of E89 on the pH-dependent conformational rearrangement of the EF loop, also known as the Tanford transition, in a series of apo-β-lactoglobulin (BLG) structures. Compared to existing studies these simulations were carried out over a much longer time scale (200 ns where the stability of the transition can be evaluated) and used an explicit water model. We considered eight different entries from the Brookhaven Protein Data Bank (PDB) separated into two groups. We observed that fixing the protonation state of E89 prompts the transition of the EF loop only when its side chain is oriented under the loop and into the entrance of the interior cavity. The motion of the EF loop occurs mostly as a step-function and its timing varies greatly from ~ 20 ns to ~170 ns from the beginning of the simulation. Once the transition is completed, the protein appears to reach a stable conformation as in a true two-state transition. We also observed novel findings. When the transition occurs, the hydrogen bond between E89 and S116 is replaced with a salt bridge with Lys residues in the βC-CD loop-βD motif. This electrostatic interaction causes the distortion of this motif as well as the protrusion of the GH loop into the aperture of the cavity with the result of limiting the increase of its contour area despite the rotation of the EF loop.

An accurate QSAR model was developed for the prediction of the anti-HIV activities of a set of DAPY-like derivatives as new non-nucleoside reverse transcriptase inhibitors (NNRTIs). The ligand–receptor (LR) interactions for all compounds were studied by the docking of compounds in the active site of appropriate receptors. The binding information of LR complexes at the best pose was called the molecular docking descriptors (MDDs). The mixture of 10 MDDs with about 154 simple, functional group counts was used as a new group of descriptors in the QSAR study of DAPY-like compounds. Among the 164 mixed descriptors, seven descriptors were selected as the most effective descriptors using the linear stepwise regression (SR) method and used as inputs of the artificial neural network (ANN) model. Levenberg–Marquardt (LM) method was used for the training of ANN through the backpropagation of errors algorithm. The Levenberg–Marquardt artificial neural network (LM-ANN) model with the architecture of 6-4-1 was selected as the optimal model. The predictability of the LM-ANN model was estimated by applying the external test set and the leave-one-out (LOO) method. The mean square errors (MSEs) and coefficient of determination (R2) values for the test set were 0.16 and 0.89, respectively. The Q2 value for the LOO method was calculated as 0.72. The results obtained demonstrated that a mixture of MDDs and functional group counts provided the required information for the construction of a QSAR model with acceptable prediction ability.

Chemoinformatic studies were carried on some inhibitors of dopamine transporter to develop a predictive and robust QSAR model and also to elucidate binding mode and molecular interactions between the ligands (inhibitors) and the receptor targeting schizophrenia as novel Antipsychotic agents. Density Functional Theory (DFT) approach was utilized to optimize the ligands at B3LYP/6-31G∗ at the ground state and Multi-linear regression of the genetic function approximation (MLR-GFA) method was employed in building Penta-parametric linear equation models. The best model with statistically significant parameters has squared correlation coefficient R2= 0.802, adjusted squared correlation coefficient R2adj = 0.767, Leave one out (LOO) cross-validation coefficient (Q2) = 0.693, lack of fit score (LOF) = 0.406, R2Test = 0.77, Y-randomization test (cR2p) = 0.714, Chi-squared (χ2) =0.026, bootstrapping (Systematic errors = 0.272) and Variance Inflation Factor (VIF), Pharmaceutical chemistry, Physical chemistry, Theoretical chemistry, Qsar; Disorder; Antipsychotic; OECD; Drug; Depression

A theoretical bioinformatic investigation was carried out on some Inhibitors of serotonin transporter (SERT) of Phenyl piperidine derivatives using Density Functional Theory (DFT/B3LYP/6-31G*) at ground state with Spartan 14 V1.1.4 software in modeling the antipsychotic activity of the compounds. The molecular descriptors were computed using the PaDEL-Descriptor software 2.20 version. Penta-parametric Multi-linear regression models were developed using the MLR-GFA for selecting the most important descriptors. The statistical parameters for the best model are; R2Train= 0.8572, R 2adj = 0.8274, R2Test = 0.678, Q2cv (LOO) = 0.7664, Ꭓ2= 0.0036, r2m (LOO)= 0.694 and Delta r2m (LOO)= 0.0051). Also, the estimated Chi-squared (Ꭓ2= 0.0036), Root-mean squared error (RMSE= 0.168) and and

We recently discussed several limitations of Bayesian leave-one-out cross-validation (LOO) for model selection. Our contribution attracted three thought-provoking commentaries. In this rejoinder, we address each of the commentaries and identify several additional limitations of LOO-based methods such as Bayesian stacking. We focus on differences between LOO-based methods versus approaches that consistently use Bayes' rule for both parameter estimation and model comparison. We conclude that LOO-based methods do not align satisfactorily with the epistemic goal of mathematical psychology.

The drug design and discovery of lipid modulators is very demanding as no new molecule has entered into the market in the last 35 years. Cholesteryl ester transfer protein (CETP) is a promising target as lipid modulators. Inhibition of the CETP enzyme reduces the risk of cardiovascular events. The first CETP inhibitor torcetrapib and related drug candidates failed in the clinical trial due to the off-target effects leading to high toxicity. Thus, newer CETP inhibitors have now paramount importance to accelerate the drug discovery efforts in the field of cardiovascular disease (CVD). In the present study, 140 benzoxazole compounds were studied by using different chemometric techniques, for example, pharmacophore mapping, molecular docking, three-dimensional quantitative structure–activity relationship comparative molecular field analysis (3D-QSAR CoMFA), topomer CoMFA and Bayesian classification, in order to generate complete and reliable information regarding the structural requirements for the CETP inhibition. The best pharmacophore hypothesis was statistically significant (regression coefficient of 0.957 and a lower root mean square of 0.890). Molecular docking study revealed that cyano-substituted compounds form hydrogen bond with targeted macromolecule. The 3D-QSAR CoMFA model also produced a leave-one-out (LOO) cross-validated Q2 of 0.527, an R2 of 0.853 and an R2Pred of 0.603. Similarly, two topomer CoMFA models were also statistically significant and reliable in terms of their Q2, R2 and R2Pred values. The Bayesian classification study also provided the excellent ROC values of 0.919 and 0.939 for training and test sets, respectively. Overall, this study may help in the rational design of newer benzoxazole type compounds with higher CETP inhibition. Communicated by Ramaswamy H. Sarma

Here we report on an advanced photoelectrochemical (PEC) oxidation of 22 phenolic pollutants based on modified nanoporous TiO2, which was directly grown on a titanium substrate electrochemically. Their degradation rate constants were experimentally determined and their physicochemical properties were computaionally calculated. The quantitative structure–property relationship (QSPR) was elucidated by employing multiple linear regression (MLR) method. A supervised machine learning approach was employed to build QSPR models. The high predictive abilities of the QSPR model were validated via leave-one-out (LOO) method and a strict regimen of statistical validation tests. The significant descriptors identified in the QSPR Model for the phenolic compounds were also assessed using a typical dye pollutant Rhodamine B, further confirming the high effectiveness and predictability of the optimized model. Our study has shown that the integrated effect of the structural, hydrophobic and topological properties along with electronic property should be considered in order to design an efficient PEC catalytic approach for environmental applications.

A set of 263 plant-derived compounds with larvicidal activity against Aedes aegypti L. (Diptera: Culicidae) vector is collected from the literature, and is studied by means of a non-conformational quantitative structure-activity relationships (QSAR) approach. The balanced subsets method (BSM) is employed to split the complete dataset into training, validation and test sets. From 26,775 freely available molecular descriptors, the most relevant structural features of compounds affecting the bioactivity are taken. The molecular descriptors are calculated through four different freewares, such as PaDEL, Mold², EPI Suite and QuBiLs-MAS. The replacement method (RM) variable subset selection technique leads to the best linear regression models. A successful QSAR equation involves 7-conformation-independent molecular descriptors, fulfiling the evaluated internal (loo, l30‰, VIF and Y-randomization) and external (test set with Ntest = 65 compounds) validation criteria. The practical application of this QSAR model reveals promising predicted values for some natural compounds with unknown experimental larvicidal activity. Therefore, the present model constitutes the first one based on a large molecular set, being a useful computational tool for identifying and guiding the synthesis of new active molecules inspired by natural products., Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas, Centro de Investigación y Desarrollo en Ciencias Aplicadas, Facultad de Ciencias Agrarias y Forestales

Theoretical modeling and molecular docking studies were carried out on cinnamic acid analogues as potent anti-tubercular agents. Theoretical models were developed to investigate the reported observed activities and modify the leading compound with better activity. Five predictive models were generated by employing Genetic Function Approximation (GFA) and Multi-linear Regression (MLR). Based on statistically significant, the optimum model with prominent validation parameters was selected. The selected model was found to have squared correlation coefficient (R2) of 0.942939, adjusted squared correlation coefficient (R2 adj) value of 0.925382 and Leave one out (LOO) cross validation coefficient (Qcv2) value of 0.893486. External validations test were carried out in order to validate the selected model and the model was found to have (R2test) of 0.8612 and Coefficient of determination for Y-randomization (cRp2) value of 0.78571. The docking studies revealed the best molecule with docking scores of −13.7 kcal/mol which formed H-bond and hydrophobic interaction and with amino acid residues M. tuberculosis cytochromes (MTB CYP121). QSAR model generated propose the direction for the design of new anti-tubercular agents via ligand based design while molecular docking results against MTB CYP121 receptor provides a valuable approach for structure based design. Keywords: Anti-tuberculosis, Docking, QSAR, Y-randomization

Lipid peroxidation of polyunsaturated fatty acids in cells may occur via their catalytic autoxidation through peroxyl radicals under oxidative stress conditions. Lipid peroxidation is related to a number of pathologies, and may be invoked in new forms of regulated cell death, yet it may also have beneficial roles in cell signaling cascades. Antioxidants are a natural line of defense against lipid peroxidation, and may accordingly impact the biological outcome associated with the redox chemistry of lipid peroxidation. Critical to unraveling the physiological and pathological role of lipid peroxidation is the development of novel probes with the partition, chemical sensitivity and more importantly, molecular specificity, enabling the spatial and temporal imaging of peroxyl radicals in the lipid membranes of live cells, reporting on the redox status of the cell membrane. This review describes our recent progress to visualize lipid peroxidation in model membrane systems and in live cell studies. Our work portrays the mechanistic insight leading to the development of a highly sensitive probe to monitor lipid peroxyl radicals (LOO•). It also describes technical aspects including reagents and fluorescence microscopy methodologies to consider in order to achieve the much sought after monitoring of rates of lipid peroxyl radical production in live cell studies, be it under oxidative stress but also under cell homeostasis. This review seeks to bring attention to the study of lipid redox reactions and to lay the groundwork for the adoption of fluorogenic antioxidant probeshancement and maximum intensity recorded in turn provide a benchmark to estimate, when compared to the control BODIPY dye lacking the intramolecular PeT based switch, the overall exte and related fluorescence microscopy methods toward gaining rich spatiotemporal information on lipid peroxidation in live cells.

Molecular docking of 22 N-aroyl-substituted halo(H)anthranilic acid amides and hydrazides with cyclooxygenase 1 (COX1) is described. The dependence of anti-inflammatory activity (AIA) on scoring functions (BeCOX1, ImeCOX1, KiCOX1) and lipophilicity constant (log Pcalc) is studied. The optimal regression equations for discovering biologically active compounds were AIAcalc = –42.1907 – 1.1029·BeCOX1–7.9806·ImeCOX1 + 8.7036·log Pcalc (R = 0.902, F = 26.32, S = 8.14, N = 22, $$ {Q}_{\mathrm{LOO}}^2 $$ = 0.73). The predicted AIAcalc values were checked for six compounds and were found to be related to the experimental AIAexp values (R = 0.909).

Modeling studies using 3D-QSAR and molecular docking methods were performed on a set of 34 hybrids of 4-aminoquinoline derivatives previously studied as effective antimalarial agents of wild type and quadruple mutant Plasmodium falciparum dihydrofolate reductase (DHFR). So, the famous mathematical method multiple linear regression (MLR) was explored to build the QSAR model. The DFT-B3LYP method with the basis set 6-31G was used to calculate the quantum chemical descriptors, chosen to represent the electronic descriptors of molecular structures. On the contrary, the MM2 method was used to calculate lipophilic, geometrical, physicochemical, and steric descriptors. The QSAR model tested with artificial neural network (ANN) method shows high performance towards its predictability. The predicted model was confirmed by three validation methods: leave-one-out (LOO) cross validation, Y-randomization, and validation external. The molecular docking study of three compounds 9, 11, and 26 on both wild and quadruple mutant types of pf-DHFR-TS as the protein target helps to understand more and then predict the binding modes with the binding sites.

Ethnopharmacological relevance Lippia origanoides H.B.K. is an aromatic species used in folk medicine to treat respiratory diseases, including asthma. Aim of the study The aim of this work was to evaluate the relaxing potential and mechanism of action of the L. origanoides (LOO) essential oil in isolated guinea-pig trachea. Materials and methods Leaves from L. origanoides were collected at experimental fields under organic cultivation, at the Forest Garden of Universidade Estadual de Feira de Santana. Essential oil was extracted by hydrodistillation, analyzed by GC/FID and GC/MS and the volatile constituents were identified. Spasmolytic activity and relaxant mechanism of LOO were assayed in isolated guinea-pig trachea contracted with histamine, carbachol or hyperpolarizing KCl. Results Chemical analysis revealed the presence of carvacrol (53.89%) as major constituent. LOO relaxed isolated guinea-pig trachea pre-contracted with KCl 60 mM [EC50 = 30.02 μg/mL], histamine 1 µM [EC50 = 9.28 μg/mL] or carbachol 1 µM [EC50 = 51.80 μg/mL]. The pre-incubation of glibenclamide, CsCl, propranolol, indomethacin, hexamethonium, aminophylline or L-NAME in histamine-induced contractions did not alter significantly the relaxant effect of LOO. However, the presence of 4-aminopyridine, tetraethylammonium or methylene blue reduced LOO effect, while the presence of dexamethasone or atropine potentialized the LOO relaxant effect. LOO pre-incubation inhibited carbachol-evoked contractions, with this effect potentialized in the presence of sodium nitroprusside and blocked in the presence of ODQ. Conclusions The relaxant mechanism of LOO on the tracheal smooth muscle possibly involves stimulating of soluble guanylyl cyclase with consequent activation of the voltage-gated and Ca2+-activated K+ channels.

BACKGROUND Tuberculosis is one of the main medical problems and some people are suffering still from this infectious disease. 1, 4-dihydropyridines are multi-target ligands that recently are recognized as anti-tubercular agents. METHODS In the current research, computational studies were conducted of some synthesized 1, 4- dihydropyridine-3, 5-dicarboxamides in non-hydrolyzed and hydrolyzed forms to find the drugreceptor interactions profile. RESULTS Among equations obtained for non-hydrolyzed compounds, the model with better statistical parameters such as R2= 0.9462, q2 of LOO= 0.8318 and q2 of LMO= 0.7987 was considered as the best one and EEig08d, LAI, Mor23m, GATS3e, Mor28m descriptors were identified as the most significant factors that affect the biological activity of non-hydrolyzed compounds. CONCLUSION In the hydrolyzed compounds, the model which has R2= 0.9731, q2 of LOO= 0.91154462 and q2 of LMO= 0.742 was considered as the best one and Mor27e and BEHv2 were also revealed as the most important descriptors in hydrolyzed compounds.

A quantitative structure-activity relationship (QSAR) study was performed to develop a model that relates the structures of 50 compounds to their activities against M. tuberculosis. The compounds were optimized by employing density functional theory (DFT) with B3LYP/6-31G⁎. The Genetic Function Algorithm (GFA) was used to select the descriptors and to generate the correlation model that relates the structural features of the compounds to their biological activities. The optimum model has squared correlation coefficient (R2) of 0.9202, adjusted squared correlation coefficient (Radj) of 0.91012, and leave-one-out (LOO) cross-validation coefficient (Qcv2) value of 0.8954. The external validation test used for confirming the predictive power of the built model has R2pred value of 0.8842. These parameters confirm the stability and robustness of the model. Docking analysis showed the best compound with high docking affinity of −14.6 kcal/mol which formed hydrophobic interaction and hydrogen bond with amino acid residues of M. tuberculosis cytochromes (Mtb CYP121). QSAR and molecular docking studies provide valuable approach for pharmaceutical and medicinal chemists to design and synthesize new anti-Mycobacterium tuberculosis compounds.

A collection of thirty-six aromatic heterocycle thiosemicarbazone analogues presented a broad span of anti-tyrosinase activities were designed and obtained. A robust and reliable two-dimensional quantitative structure–activity relationship model, as evidenced by the high q 2 and r 2 values (0.848 and 0.893, respectively), was gained based on the analogues to predict the quantitative chemical-biological relationship and the new modifier direction. Inhibitory activities of the compounds were found to greatly depend on molecular shape and orbital energy. Substituents brought out large ovality and high highest-occupied molecular orbital energy values helped to improve the activity of these analogues. The molecular docking results provided visual evidence for QSAR analysis and inhibition mechanism. Based on these, two novel tyrosinase inhibitors O04 and O05 with predicted IC 50 of 0.5384 and 0.8752 nM were designed and suggested for further research.

The Lush Science Prize 2016 was awarded to Daniele Zink and Lit-Hsin Loo for the interdisciplinary and collaborative work between their research groups in developing alternative methods for the prediction of nephrotoxicity in humans. The collaboration has led to the establishment of a series of pioneering alternative methods for nephrotoxicity prediction, which includes: predictive gene expression markers based on pro-inflammatory responses; predictive in vitro cellular models based on pluripotent stem cell-derived proximal tubular-like cells; and predictive cellular phenotypic markers based on chromatin and cytoskeletal changes. A high-throughput method was established for chemical testing, which is currently being used to predict the potential human nephrotoxicity of ToxCast compounds in collaboration with the US Environmental Protection Agency. Similar high-throughput imaging-based methodologies are currently being developed and adapted by the Zink and Loo groups, to include other human organs and cell types. The ultimate goal is to develop a portfolio of methods accepted for the accurate prediction of human organ-specific toxicity and the consequent replacement of animal experiments.

A study of quantitative structure-activity relationship (QSAR) is applied to a set of 24 molecules derived from diarylaniline to predict the anti-HIV-1 biological activity of the test compounds and find a correlation between the different physic-chemical parameters (descriptors) of these compounds and its biological activity, using principal components analysis (PCA), multiple linear regression (MLR), multiple non-linear regression (MNLR) and the artificial neural network (ANN). We accordingly proposed a quantitative model (non-linear and linear QSAR models), and we interpreted the activity of the compounds relying on the multivariate statistical analysis. The topological descriptors were computed with ACD/ChemSketch and ChemBioOffice14.0 programs. A correlation was found between the experimental activity and those obtained by MLR and MNLR such as (Rtrain = 0.886 ; R2train = 0.786) and (Rtrain = 0.925 ; R2train = 0.857) for the training set compounds, and (RMLR-test = 0.6) and (RMNLR-test = 0.7) for a randomly chosen test set of compounds, this result could be improved with ANN such as (R = 0.916 and R2 = 0.84) with an architecture ANN (6-1-1). To evaluate the performance of the neural network and the validity of our choice of descriptors selected by MLR and trained by MNLR and ANN, we used cross-validation method (CV) including (R = 0.903 and R2 = 0.815) with the procedure leave-one-out (LOO). The results showed that the MLR and MNLR have served to predict activities, but when compared with the results given by a 6-1-1 ANN model. We realized that the predictions fulfilled by the latter model were more effective than the other models. The statistical results indicated that this model is statistically significant and showing a very good stability towards the data variation in leave-one-out (LOO) cross validation.