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

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

Design of new α-glucosidase inhibitors through a combination of 3D-QSAR, ADMET screening, molecular docking, molecular dynamics simulations and quantum studies

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