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Fungal infections are a growing public health problem worldwide. Despite the availability of several medicines, their efficacy is still constrained by fungal resistance. This research conducted the 2D/3D-QSAR analysis on twenty-nine triazole molecules previously evaluated for their antifungal activity. The HQSAR/B-H, CoMFA and CoMSIA models were built using twenty-three molecules in the training set. They show high Q2 values (0.646, 0.564 and 0.561, respectively) and important R2 values (0.764, 0.805 and 0.787, respectively). The predictive capacity of the established models was validated by external validation; they performed well. The contour maps derived from the HQSAR/B-H, CoMFA and CoMSIA models provide more detail to identify favorable and unfavorable groupings impacting the activity. Then, 4 proposed new triazole molecules with significant antifungal activity were suggested. In addition, the molecular docking results showed good binding energies and interactions of the proposed inhibitors in the active site of the receptor studied. The molecular dynamics and MM/PBSA methods confirmed and validated the molecular docking results. The new triazole molecules were evaluated for their oral bioavailability and toxicity using ADME/Tox properties. Finally, the retrosynthesis method created a synthetic pathway for the candidate inhibitor Z1.

Fungal infections profoundly affect human health, causing a substantial number of infections and millions of fatalities annually on a global scale. The identification of new drugs targeting this infection is a challenge that is not yet complete. Natural products, including medicinal and aromatic plants, substances that act as sources of beneficial chemical compounds for the development of efficient therapies, are among the medicines that can be used to combat this type of infection. In this study, seven bioactive molecules derived from garlic plant as potential antifungal inhibitors were investigated using computational methods. Alliin and S-allyl-cysteine, bioactive molecules generated from garlic, showed good stability at the active site of the studied receptor (PDB code: 5TZ1). They provided binding energies of −4.80 and −4.90 Kcal/mol, and inhibition constant (Ki) values of 303.78 and 253.68 µM, respectively. Similarly, alliin and S-allyl-cysteine were stabilized in the active site of the target receptor by conventional hydrogen bonds with residues Ser507 (2.47 Å), Ser378 (3.01 Å), Met508 (2.62 Å, 3.46 Å), and His377 (3.00 Å), Ser378 (3.09 Å), Met508 (2.01 Å), Ser507 (2.26 Å), respectively. These results were confirmed by molecular dynamic simulation. The selected molecules comply with the most important drug rules such as Lipinski, Veber and Egan, have good ADME properties and are not toxic; therefore, these bioactive molecules have good pharmacokinetic properties and bioavailability. The retrosynthesis method has created a pathway for the synthesis of these candidate inhibitors. As a result, the outcomes of this study strongly suggest that Alliin, S-allyl-cysteine, are potential antifungal inhibitors in the future.

Diabetes mellitus is a chronic and non-infectious metabolic disorder caused by insufficient insulin secretion. This study investigated a set of thirty-one 4-amino-1,2,4-triazole derivatives, experimentally evaluated for their α-glucosidase activity against diabetes mellitus, using the three-dimensional quantitative structure–activity relationship (3D-QSAR) approach. The recommended CoMFA and CoMSIA/EHA models showed good predictive ability, manifested by high R2 values and important Q2 values. The molecular structural features offered by the CoMFA and CoMSIA/EHA contour maps had a significant impact on the determination of appropriate groups to enhance activity. Hence, four new 4-amino-1,2,4-triazole inhibitors were proposed and designed with good predicted α-glucosidase activity. The pharmacological and ADME-Tox properties of the four recommended molecules were predicted and examined. Molecular docking studied the interaction modes between the targeted receptor and 4-amino-1,2,4-triazole derivatives; it showed good stability for the new title molecule M1. Furthermore, molecular dynamics simulation at 100 ns and MM/PBSA approach results demonstrated an acceptable stability and the interactive force of the compound M1. Finally, the most nucleophilic and electrophilic centers of the compounds C25 and M1 were determined using quantum analysis. The current work encourages further experimental and scientific research on M1 molecule as a potent α-glucosidase inhibitor.

The worldwide prevalence of cancer and its increasing frequency make it a key research area in drug discovery programs. The current research paper describes the development of QSAR models based on the in vitro against topoisomerase II, which identified the structural origin of anticancer activity for derivatives of triazole moieties linked to mansonone E. The models PLS regression QSARs validated by LOO showed an R2 of 0.92, 0.89 and 0.99 and a Q2 of 0.75, 0.62 and 0.88 for CoMFA, CoMSIA and HQSAR respectively. External validation criteria were used to validate the reliability of the models. These results show the impact of electrostatic and steric fields and of the hydrogen bond donor on the activity of the compounds studied. Based on these results, seven novel inhibitors with high activity were designed, which successfully passed Lipinski's rule of five for oral bioavailability. The evaluations of ADME/Tox parameters and synthetic accessibility for chemical synthesis showed acceptable results. Ligand interactions in binding site protein were assessed using molecular docking. The results show the correct conformational pose of the designed compounds especially the compound T1 where it forms hydrogen and hydrophobic interactions with the main binding site residues. The stability of the complexes was confirmed by the MD study and the calculation of the free binding energy. The T1 synthesis reaction was carried out according to the 1,3 cycloaddition reaction. The study of the local and global reactivity and the energy of activation of this reaction have shown the predicted of the regioselectivity of compound T1. Also we have described the state of transition of two isomers T1,4 and T1.5. Finally, this study would be interesting to help identify and optimize avenues for early discovery of anticancer drugs.

In this study, using both 2D-QSPR and 3D-QSPR approaches, to understand the structure-odor relationship of 78 1–4-pyrazine odorant molecules and to use this knowledge for the design of new food and flavor products. According to our results, the developed models have good predictability such as the HQSPR/BC model with QLOO2=0.832, R2 = 0.916, CoMSIA/SEH model with Q2 = 0.624, Rcv2 = 0.590, Rncv2 = 0.932, Rbs2 = 0.963, and Topomer CoMFA model withRtraining2 = 0.899, Rtest2 = 0.916. The Monte Carlo method was used in the creation of a Quantitative Structure-Property Relationship (QSPR) model. The molecular structure is represented using optimized Simplified Molecular Input Line Entry System (SMILES) and molecular descriptors. The performance of the model is evaluated using the Correlation Ideality Index (IIC) and the Correlation Contradiction Index (CCI). The best model, designated as TF2, boasts excellent statistical properties with a training R-squared value of 0.957 and a test R-squared value of 0.834. The model was then used to determine promoter activity levels, which formed the basis for the design of 36 new odorant molecules. Molecular docking and pharmacokinetic properties were used to explain the mode of binding between the proposed compounds and the active site of the Porcine Odorant Binding Protein complexed with pyrazine (2-isobutyl-3-methoxypyrazine). Molecular dynamic simulation was used to assess and justify the stability of the ligand in the active site of the receptor. The results of this study provide a basis for the discovery of new compounds with lower olfactory thresholds and diverse pharmacological properties.

In the present study, a Molecular Docking and in silico ADMET analysis were performed to identify the possible inhibitory effect of 23 molecules, pyrazole and pyrazolines derivatives, on Escherichia coli and to predict the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of all compounds. According to the results, every compound examined might bind to this bacterium's active site (PDB: 1FJ4). The results obtained in silico demonstrated that Only 4 (M6, M17, M19 and M20) of the 23 compounds were selected due to their inhibitory action and proximity to the important catalytic residues Thr302, Thr300, Val270, and His298 of the major protease and could be considered as orally active drug candidates due to their physical and chemical properties. The compounds M6, M17, M19 and M20 were subjected to Lipinski’s rule of five because it has the best binding affinity score in the binding study of the compound with the protein (-9.6, −9.3, −9.5, −10.3 Kcal/mol) successively. Pyrazole derivatives and the structure of pyrazolines are also effectively discussed in this paper for potential application as antibacterial agents due to their significant inhibitory activity. We were also able to predict a new potential inhibitor against a target of interest because to the result that we obtained.

Introduction: The new species of coronaviruses (CoVs), SARS-CoV-2, was reported as responsible for an outbreak of respiratory disease. Scientists and researchers are endeavoring to develop new approaches for the effective treatment against of the COVID-19 disease. There are no finally targeted antiviral agents able to inhibit the SARS-CoV-2 at present. Therefore, it is of interest to investigate the potential uses of levamisole derivatives, which are reported to be antiviral agents targeting the influenza virus. Methods: In the present study, 12 selected levamisole derivatives containing imidazo[2,1-b]thiazole were subjected to molecular docking in order to explore the binding mechanisms between these derivatives and the SARS-CoV-2 Mpro (PDB: 7BQY). The levamisole derivatives were evaluated for in silico ADMET properties for wet-lab applicability. Further, the stability of the best-docked complex was checked using molecular dynamics (MD) simulation at 20 ns. Results: Levamisole derivatives and especially molecule N°6 showed more promising docking results, presenting favorable binding interactions as well as better docking energy compared to chloroquine and mefloquine. The results of ADMET prediction and MD simulation support the potential of the molecule N°6 to be further developed as a novel inhibitor able to stop the newly emerged SARS-CoV-2. Conclusion: This research provided an effective first line in the rapid discovery of drug leads against the novel CoV (SARS-CoV-2).

The purpose of the paper is to discuss the various methods and computational approaches, which are used in computer-aided drug design. For this reason, pyrimidine and azaindole derivatives have been used to study the inhibitory activity of CaMKKII. It is an enzyme that enters the brain to greatly reduce food from regulating the production of Ghrelin that is synthesized by the stomach and acts on the hypothalamus. The obtained results from different techniques such as the 3D-QSAR, molecular docking, and ADMET were applied to study series of new CaMKKII inhibitors of 23 molecules based on pyrimidine and azaindole derivatives. The CoMFA and CoMSIA models were used in 19 molecules in the training set that give high values of determination coefficient R2 0.970 and 0.902 respectively, and significant values of Leave-One-Out cross-validation coefficient Q2 0.614 and 0.583 respectively. The predictive capacity of this model was examined by external validation though using a test set of four compounds with a predicted determination coefficient test R2ext of 0.778 and 0.972 successively. The method of alignment adapted with the appropriate parameters gave credible models. The CoMFA and CoMSIA models produce the contour maps which were used to define a 3D-QSAR mode. DOI: http://dx.doi.org/10.17807/orbital.v13i4.1608

COVID-19 pandemic caused by very severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) agent is an ongoing major global health concern. The disease has caused more than 452 million affected cases and more than 6 million death worldwide. Hence, there is an urgency to search for possible medications and drug treatments. There are no approved drugs available to treat COVID-19 yet, although several vaccine candidates are already available and some of them are listed for emergency use by the world health organization (WHO). Identifying a potential drug candidate may make a significant contribution to control the expansion of COVID-19. The in vitro biological activity of asymmetric disulfides against coronavirus through the inhibition of SARS-CoV-2 main protease (Mpro) protein was reported. Due to the lack of convincing evidence those asymmetric disulfides have favorable pharmacological properties for the clinical treatment of Coronavirus, in silico evaluation should be performed to assess the potential of these compounds to inhibit the SARS-CoV-2 Mpro.In this context, we report herein the molecular docking for a series of 40 unsymmetrical aromatic disulfides as SARS-CoV-2 Mpro inhibitor. The optimal binding features of disulfides within the binding pocket of SARS-CoV-2 endoribonuclease protein (Protein Data Bank [PDB]: 6LU7) was described. Studied compounds were ranked for potential effectiveness, and those have shown high molecular docking scores were proposed as novel drug candidates against SARS-CoV-2. Moreover, the outcomes of drug similarity and ADME (Absorption, Distribution, Metabolism, and Excretion) analyses have may have the effectiveness of acting as medicines, and would be of interest as promising starting point for designing compounds against SARS-CoV-2. Finally, the stability of these three compounds in the complex with Mpro was validated through molecular dynamics (MD) simulation, in which they displayed stable trajectory and molecular properties with a consistent interaction profile.

Studies and scientific research indicate that the platelet-activating factor (PAF) is a major pro-inflammatory mediator in the initiation and development of cancer. There is also evidence confirming that PAF is an integral part of suppressing the immune system and promoting the appearance of a malignant tumor. For this reason, it is useful to analyze the molecular docking data of eleven flavonoids derivatives isolated from the active leaf extracted from chromolaena odorata with their anti-PAF activity. As a result, it is evident that the natural product of flavonoids may have a positive effect in the development of both therapeutic and preventive agents for platelet activating factor (PAF) antagonist and suggests potential guidelines for the design of PAF inhibitors. Based on the docking score analysis, drug likeness study, and ADMET prediction. We found that six compounds respect all drug-likeness rules and can be used as a potent molecule for inhibition of platelet activating factor (PAF).

In search of novel and more potent p-aminobenzoic acid derivatives previously evaluated as effective acetylcholinesterase inhibitors for the control of Alzheimer’s disease (AD), an integrated computational approach of three-dimensional quantitative structure–activity relationship and molecular docking were performed on a series of 20 compounds. The 3D-QSAR approach was applied to statistically study the structure-activity relationships (SAR) and had yielded good statistical significance for two high predictive models; comparative molecular field analysis (CoMFA: Q2=0.785; R2=0.936; rext2= 0.818) and comparative molecular similarity indices analysis (CoMSIA: Q2=0.831; R2=0.944; rext2= 0.931). Detailed analysis of the predictive models contour maps revealed that the hydrophobic and electrostatic fields govern the bioactivity and provided much helpful information to understand the features requirement in order to develop new potent acetylcholinesterase inhibitors. These findings were very useful for designing four novel inhibitors with enhanced activities targeting acetylcholinesterase. Through molecular docking, the newly designed compounds and compound 19 were docked on AChE as the protein target which helped to analyze the interaction characteristics and explore the binding modes at the active sites of the AChE. This work may be of utility for guiding the rational design of a new generation of acetylcholinesterase inhibitors. DOI: http://dx.doi.org/10.17807/orbital.v12i4.1467

Cancer is one of the world's causes of death, which requires the discovery of new molecules likely to become anticancer drugs. In this study, a three–dimensional quantitative structure-activity relationship is employed to study eighteen compounds of 5,5-diphenylimidazolidine-2,4-dione derivatives against cancer cell lines HeLa, their pIC50 varied from 3.62 to 5.00. In addition, the 3D-QSAR model was defined on the basis of Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices (CoMSIA) analysis, the model achieved strong predictability with the model CoMFA is (Q2 =0.70; R2 =0.94; r2 test =0.96) and the best one on CoMSIA (Q2 =0.73; R2 =0.97; r2 test= 0.98), respectively. We have proposed four compounds with highly potent anticancer predicted activities, based on successful results obtained by the contour maps formed by the method model. Furthermore, the ADMET properties of these newly designed compounds were in silico evaluated, among which two derivatives have respected these properties. These compounds were further evaluated by molecular docking, showing that two molecules T2 and T4 exhibit favorable interactions with the targeted receptor and a high total score. These findings may afford valuable more information to design compounds anticancer activity against Hela cells. DOI: http://dx.doi.org/10.17807/orbital.v14i1.1659

In this study, we have established two-dimensional quantitative structure propriety relationships (2D-QSPR) model, for a group of 78 molecules based on pyrazine, these molecules were subjected to a 2D-QSPR analyze for their odors thresholds propriety using stepwise Multiple Linear Regression (MLR). The 35 parameters are calculated for the 78 studied compounds using the Gaussian 09W, ChemOffice and ChemSketch softwares. Quantum chemical calculations are used to calculate electronic and quantum chemical descriptors, using the density functional theory (B3LYP/6-31G (d) DFT) methods. The model was used to predict the odors thresholds propriety of the test and training set compounds, and the statistical results exhibited high internal and external consistency as demonstrated by the validation methods.

Purpose: In this review, a set of aryl halides analogs were identified as potent checkpoint kinase1 (Chk1) inhibitors through a series of computer-aided drug design processes, to develop modelswith good predictive ability, highlight the important interactions between the ligand and theChk1 receptor protein and determine properties of the new proposed drugs as Chk1 inhibitorsagents.Methods: Three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling,molecular docking and absorption, distribution, metabolism, excretion and toxicity (ADMET)approaches are used to determine structure activity relationship and confirm the stableconformation on the receptor pocket.Results: The statistical analysis results of comparative -molecular field analysis (CoMFA) andcomparative molecular similarity indices analysis (CoMSIA) models that employed for a trainingset of 24 compounds gives reliable values of Q2 (0.70 and 0.94, respectively) and R2 (0.68 and0.96, respectively).Conclusion: Computer–aided drug design tools used to develop models that possess goodpredictive ability, and to determine the stability of the observed and predicted molecules in thereceptor pocket, also in silico of pharmacokinetic (ADMET) results shows good properties andbioavailability for these new proposed Chk1 inhibitors agents.

Coronavirus (COVID-19), an enveloped RNA virus, primarily affects human beings. It has been deemed by the World Health Organization (WHO) as a pandemic. For this reason, COVID-19 has become one of the most lethal viruses which the modern world has ever witnessed although some established pharmaceutical companies allege that they have come up with a remedy for COVID-19. To that end, a set of carboxamides sulfonamide derivatives has been under study using 3D-QSAR approach. CoMFA and CoMSIA are one of the most cardinal techniques used in molecular modeling to mold a worthwhile 3D-QSAR model. The expected predictability has been achieved using the CoMFA model (Q2 = 0.579; R2 = 0.989; R2test = 0.791) and the CoMSIA model (Q2 = 0.542; R2 = 0.975; R2test = 0.964). In a similar vein, the contour maps extracted from both CoMFA and CoMSIA models provide much useful information to determine the structural requirements impacting the activity; subsequently, these contour maps pave the way for proposing 8 compounds with important predicted activities. The molecular surflex-docking simulation has been adopted to scrutinize the interactions existing between potentially and used antimalarial molecule on a large scale, called Chloroquine (CQ) and the proposed carboxamides sulfonamide analogs with COVID-19 main protease (PDB: 6LU7). The outcomes of the molecular docking point out that the new molecule P1 has high stability in the active site of COVID-19 and an efficient binding affinity (total scoring) in relation with the Chloroquine. Last of all, the newly designed carboxamides sulfonamide molecules have been evaluated for their oral bioavailability and toxicity, the results point out that these scaffolds have cardinal ADMET properties and can be granted as reliable inhibitors against COVID-19.

A theoretical study of 1,3-cycloaddition has been carried out using density functional theory (DFT) methods at the B3LYP/6-31G* level. The regioselectivity of the reaction have been clarified through different theoretical approaches: Case of a Two-Center Process (Domingo approach), HSAB principle (Gazquez and Mendez approach), and the activation energy calculations. The analysis of results shows that the reaction takes place along concerted asynchronous mechanism and the isomer meta is favored, in agreement with the experiment results. DOI: http://dx.doi.org/10.17807/orbital.v9i5.1017

The ACD/ChemSketch, MarvinSketch, and ChemOffice programmes were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). The best descriptors were selected to establish the Quantitative Structure-Property Relationship (QSPR) of the retention/release property of odorant molecules in pectin gels using Principal Components Analysis (PCA), Multiple Linear Regression (MLR), Multiple Non-linear Regression (MNLR) and Artificial Neural Network (ANN) methods We propose a quantitative model based on these analyses. PCA has been used to select descriptors that exhibit high correlation with the retention/release property. The MLR method yielded correlation coefficients of 0.960 and 0.958 for PG-0.4 (pectin concentration: 0.4% w/w) and PG-0.8 (pectin concentration: 0.8% w/w) media, respectively. Internal and external validations were used to determine the statistical quality of the QSPR of the two MLR models. The MNLR method, considering the relevant descriptors obtained from the MLR, yielded correlation coefficients of 0.978 and 0.975 for PG-0.4 and PG-0.8 media, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outside compounds. The effects of different descriptors on the retention/release property are described, and these descriptors were used to study and design new compounds with higher and lower values of the property than the existing ones. Keywords: Odorant Molecules, Retention/Release, Pectin Gels, Quantitative Structure Property Relationship, Multiple Linear Regression, Artificial Neural Network

An integrated approach physicochemistry and structures property relationships has been carried out to study the odorant molecules retention/release phenomenon in the water. This study aimed to identify the molecular properties (molecular descriptors) that govern this phenomenon assuming that modifying the structure leads automatically to a change in the retention/release property of odorant molecules. ACD/ChemSketch, MarvinSketch, and ChemOffice programs were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). A total of 37 molecules (2/3 of the data set) were placed in the training set to build the QSPR models, whereas the remaining, 14 molecules (1/3 of the data set) constitute the test set. The best descriptors were selected to establish the quantitative structure property relationship (QSPR) of the retention/release property of odorant molecules in water using multiple linear regression (MLR), multiple non-linear regression (MNLR) and an artificial neural network (ANN) methods. We propose a quantitative model according to these analyses. The models were used to predict the retention/release property of the test set compounds, and agreement between the experimental and predicted values was verified. The descriptors showed by QSPR study are used for study and designing of new compounds. The statistical results indicate that the predicted values are in good agreement with the experimental results. To validate the predictive power of the resulting models, external validation multiple correlation coefficient was calculated and has both in addition to a performant prediction power, a favorable estimation of stability. DOI: http://dx.doi.org/10.17807/orbital.v9i4.978

Abstract Background Quantitative structure activity relationship was carried out to study a series of PIM1 and PIM2 inhibitors. The present study was performed on twenty-five substituted 5-(1H-indol-5-yl)-1,3,4-thiadiazols as PIM1 and PIM2 inhibitors having pIC50 ranging from 5.55 to 9 µM and from 4.66 to 8.22 µM, respectively, using genetic function algorithm for variable selection and multiple linear regression analysis (MLR) to establish unambiguous and simple QSAR models based on topological molecular descriptors. Results Results showed that the MLR predict activity in a satisfactory manner for both activities. Consequently, the aim of the current study is twofold, first, a simple linear QSAR model was developed, which could be easily handled by chemist to screen chemical databases, or design for new potent PIM1 and PIM2 inhibitors. Second, the outcomes extracted from the current study were exploited to predict the PIM inhibitory activity of some studied compound analogues. Conclusions The goal of this study is to develop easy and convenient QSAR model could be handled by everyone to screen chemical databases or to design newly PIM1 and PIM2 inhibitors derived from 5-(1H-indol-5-yl)-1,3,4-thiadiazol. Graphical abstract Flow chart of the methodology used in this work.

For their biological properties and particularly for their anticancer activities, chalcones are widely studied. In this work, we have submitted diverse sets of chalcone derivatives to the 3D-QSAR (3-dimensional quantitative structural-activity relationship) to study their anticancer activities against HTC116 (human colon cancer), relying on the 3-dimensional descriptors: steric and electrostatic descriptors for the CoMFA (comparative molecular field analysis) method and steric, electrostatic, hydrophobic, H-bond donor, and H-bond acceptor descriptors for the CoMSIA method. CoMFA as well as the CoMSIA model have encouraging values of the cross-validation coefficient (Q2) of 0.608 and 0.806 and conventional correlation coefficient (R2) of 0.960 and 0.934, respectively. Furthermore, values of R2test have been obtained as 0.75 and 0.90, respectively. Besides, y-randomization test was also performed to validate our 3D-QSAR models. Based on these satisfactory results, ten new compounds have been designed and predicted by in silico ADMET method. This study could expand the understanding of chalcone derivatives as anticancer agents and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity.

In this study, we have selected a series of a new family of molecules bearing Triazolo-benzodiazepines, an eleven membered heterocyclic ring has been studied for antidepression activity. Docking studies suggested that all the eleven ligands interacted well within active site of Drosophila melanogaster dopamine transporter (dDAT) (PDB ID: 4M48). Most ligands formed H-bond with amino acid Phe43, Asp46, Asp475, Tyr123, Ser421 and/or Gln316 and also exhibited Pi and Pi-Pi interactions with amino acid residues Tyr124, Phe319, Phe43, Phe325, Ala479 and Val120. In silico ADME evaluations of compounds showed more than 96% intestinal absorption for all compounds. During in vitro Toxicity properties prediction, the Triazolo-benzodiazepines derivatives: M1, M2, M3 and M11 showed less toxicity than the other studied molecules against algae, for daphnia the molecules M1, M2, M3, M8, M10 and M11 showed less toxicity than the reference molecule (Nortriptyline).

The quantitative structure-activity relationship (QSAR) of sixty 2-phenylimidazopyridines derivatives with anti-Human African Trypanosomiasis (anti-HAT) activity has been studied by using the density functional theory (DFT) and statistical methods. Becke's three-parameter hybrid method and the Lee-Yang-Parr B3LYP functional employing 6–31G(d) basis set are used to calculate quantum chemical descriptors using Gaussian 03W software, and the five Lipinski's parameters were calculated using ChemOffice software.In order to obtain robust and reliable QSAR model, the original dataset was randomly divided into training and prediction sets comprising 48 and 12 compounds, respectively. An optimal model for the training set with significant statistical quality was established. The same model was further applied to predict pEC50 values of the 12 compounds in the test set, further showing that this QSAR model has high predictive ability. It is very interesting to find that the anti-HAT of these compounds appear to be mainly governed by four factors, i.e., the number of H-bond donors, the lowest unoccupied molecular orbital energy, the molecular weight and the octanol/water partition coefficient. Here the possible action mechanism of these compounds was analysed and discussed, in particular, important structural requirements for great anti-HAT activity will be by increasing molecular size and substitute the 2-phenylimidazopyridines derivatives with polar, ionic, stronger accepting electron ability group and heteroatoms attached to one or more hydrogen atoms. Based on this proposed QSAR model, some new compounds with higher anti-HAT activities have been theoretically designed. Such results can offer useful theoretical references for future experimental works.

This study gives a quantitative structure–activity relationship (QSAR) analysis of toxicity of phenols and thiophenols to Photobacterium phosphoreum, which is an important indicator for water quality. The chemical structures of 51 phenols and thiophenols have been characterized by electronic and physic-chemical descriptors. The present study was performed using principal components analysis (PCA), multiple regression analysis (MLR) and artificial neural network (ANN). The quantitative model was accordingly proposed and the toxicity of the compounds was interpreted based on the multivariate statistical analysis. This study shows that the results obtained by MLR were suitable and have served to predict toxicity, but compared to the results of the ANN model, we conclude that the prediction achieved by the latter is more effective and better than MLR model. The statistical results of the predictive performance indicate that the ANN model is statistically significant than the previous reported model based on CART-LS-SVR. Following to the obtained results, our proposed model may be useful to predict of toxicity and risk assessment of chemicals.

The DFT-B3LYP method, with the base set 6-31G (d), was used to calculate several quantum chemical descriptors of 44 substituted flavonoids. The best descriptors were selected to establish the quantitative structure activity relationship (QSAR) of the inhibitory activity against aldose reductase using principal components analysis (PCA), multiple regression analysis (MLR), nonlinear regression (RNLM) and an artificial neural network (ANN). We propose a quantitative model according to these analyses, and we interpreted the activity of the compounds based on the multivariate statistical analysis. This study shows that the MLR and MNLR predict activity, but compared to the results of the ANN model, we conclude that the predictions achieved by the latter are more effective and better than the other models. The results indicate that the ANN model is statistically significant and shows very good stability toward data variation for the validation method. The contribution of each descriptor to the structure–activity relationship was also evaluated.

A study of structure–activity relationship (QSAR) was performed on a set of 30 coumarin-based molecules. This study was performed using multiple linear regressions (MLRs) and an artificial neural network (ANN). The predicted values of the antioxidant activities of coumarins were in good agreement with the experimental results. Several statistical criteria, such as the mean square error (MSE) and the correlation coefficient (R), were studied to evaluate the developed models. The best results were obtained with a network architecture [8-4-1] (R = 0.908, MSE = 0.032), activation functions (tansig–purelin) and the Levenberg–Marquardt learning algorithm. The model proposed in this study consists of large electronic descriptors that are used to describe these molecules. The results suggested that the proposed combination of calculated parameters may be useful for predicting the antioxidant activities of coumarin derivatives.

DFT-B3LYP method, with the basis set 6-31G (d), was employed to calculate nine quantum chemical descriptors of 16 acridin-9-(10H)-ones substituted with amino or (1,3-benzothiazol-2-yl)-amino groups compounds. The above descriptors were used to establish a Quantitative Structure Activity Relationship (QSAR) of the Anti-proliferative towards human monocytes activity of these compounds by Multiple Linear Regression (MLR), Multiple Non Linear Regression (MNLR) and Artificial Neural Network (ANN). The statistical results indicate that the correlation coefficients R were 0.864, 0.908 and 0.844 respectively. Results showed that the three modeling methods can provide a good prediction of the studied activity and may be useful for predicting the bioactivity of new compounds of similar class, and showed that the Multiple Non Linear Regression (MNLR) results have substantially better predictive capability than the MLR and ANN. The statistical results indicate that the models are statistically significant and show very good stability towards data variation in leave one out cross validation. DOI: http://dx.doi.org/10.17807/orbital.v7i2.677

In order to investigate the relationship between activities and structures, a 3D-QSAR study is applied to a set of 43 molecules based on triazines. This study was conducted using the principal component analysis (PCA) method, the multiple linear regression method (MLR) and the artificial neural network (ANN). The predicted values of activities are in good agreement with the experimental results. The artificial neural network (ANN) techniques, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.9 with an 8-3-1 ANN model which is a good result. As a result of quantitative structure–activity relationships, we found that the model proposed in this study is constituted of major descriptors used to describe these molecules. The obtained results suggested that the proposed combination of several calculated parameters could be useful to predict the biological activity of triazine derivatives.

DFT-B3LYP method, with the basis set 6-31G (d), was employed to calculate nine quantum chemical descriptors of 16 acridin-9-(10H)-ones substituted with amino or (1,3-benzothiazol-2-yl)-amino groups compounds. The above descriptors were used to establish a Quantitative Structure Activity Relationship (QSAR) of the Anti-proliferative towards human monocytes activity of these compounds by Multiple Linear Regression (MLR), Multiple Non Linear Regression (MNLR) and Artificial Neural Network (ANN). The statistical results indicate that the correlation coefficients R were 0.864, 0.908 and 0.844 respectively. Results showed that the three modeling methods can provide a good prediction of the studied activity and may be useful for predicting the bioactivity of new compounds of similar class, and showed that the Multiple Non Linear Regression (MNLR) results have substantially better predictive capability than the MLR and ANN. The statistical results indicate that the models are statistically significant and show very good stability towards data variation in leave one out cross validation. DOI: http://dx.doi.org/10.17807/orbital.v7i2.677