2D and 3D-QSAR/CoMSIA Comparative Study On a Series of Thiazole Derivatives as SDHI Inhibitors

A database includes compounds based on thiazole derivatives having values of Succinate dehydrogenase inhibitors against S. sclerotiorum (ssSDH) pEC50 was used to develop a structure-activity relationship using 2D and 3D-QSAR methods. The data set used was randomly distributed into 80% as a learning set and 20% to assess the external prediction of the selected models (test set). the reliability and the predictive power of the established models were examined by various methods of internal validation, external validation, and randomized Y test. To detect outliers, the applicability domain was used using the Williams plot.The 2D-QSAR results revealed that the best 2D-QSAR model was established using the multiple linear regression method (MLR) (giving R²= 0.80 and Q²= 0.63), and the partial least squares regression method (PLS) (giving R² = 0.78 and Q² = 0.64), with four descriptors: J, Log P, NRB and MD. These models have successfully passed all external and internal validation criteria.The 3D-QSAR results show that the best model selected using the molecular field analysis method (CoMSIA), giving R² = 0.957, Q² = 0.614, and R²test = 0.80. The analysis of the CoMSIA contour maps shows the nature and the position of certain structural indicators important for the improvement of the studied biological activity such as the steric and electrostatic and hydrophobic substituents, as well as the substitutes of hydrogen bonds donors. These results will also be useful for the development of new thiazole derivatives with very high pEC50 values

Inhibition activity of triazoles as a new family for the inhibition of the Indoleamine 2,3-dioxygenase 1 IDO1 protein using 2D-QSAR approach

Protein IDO1 (indoleamine 2,3-dioxygenase) occupies a critical position in the regulation of the immune system and is involved in cancer progression and the development of immune diseases. Being a therapeutic target for such critical diseases, we aimed to investigate the IDO1 inhibition activity of thirty-nine triazole derivatives using a quantitative structure-activity relationship. The dataset was under principal component analysis, multiple linear regression, and multiple non-linear regression from which two models were generated. The best 2D-QSAR model was generated using linear regression, demonstrating a determination coefficient of R2=0.680, a good acceptable internal cross-validated coefficient of R2cv=0.700, an error of MSE=0.074, and a good predictive potential of R2test=0.809. The QSAR model was further investigated using the applicability domain, which showed that all molecules were within the applicability domain, hence the absence of an outlier. Overall, the obtained results provide a reliable and highly predictive model for the design and prediction of new IDO1 inhibitors thereby influencing cancer progression and autoimmune disease development

Theoretical Study of 1,3-Dipolar Cycloadditions Regioselectivity of Benzyl Azide with Glycosyl-O Acetylene Using Density Functional Theory (DFT)

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 </p

QSAR studies on PIM1 and PIM2 inhibitors using statistical methods: a rustic strategy to screen for 5-(1H-indol-5-yl)-1,3,4-thiadiazol analogues and predict their PIM inhibitory activity

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

Computational integration for antifungal 1,2,4-triazole inhibitors design: QSAR, molecular docking, molecular dynamics simulations, ADME/Tox, and retrosynthesis studies

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

Garlic as an effective antifungal inhibitor: A combination of reverse docking, molecular dynamics simulation, ADMET screening, DFT, and retrosynthesis studies

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

Structure-odor relationship in pyrazines and derivatives: A physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, molecular docking, ADME-Tox and molecular dynamics

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