Computational evaluation of 1,2,3-triazole-based VEGFR-2 inhibitors: anti-angiogenesis potential and pharmacokinetic assessment.

The vascular endothelial growth factor (VEGF) and its cell surface receptor, as well as the human VEGFR-2 domain kinase, are some of the signaling pathways that have received the most attention in this field. This study aimed to identify novel molecules as VEGFR-2 inhibitors using 3D-QSAR modeling based on 1,2,3-triazole. Docking studies and dynamic simulations were performed to analyze novel interactions with the inhibitors and validate the molecular docking, dynamic simulations, and ADMET analyses. The optimized CoMSIA/SEH model showed good statistical results, and molecular docking and molecular dynamics simulations demonstrated stability of M3 ligand with the receptor and provided insight into ligand-receptor interactions. The newly developed compounds performed well in ADMET evaluations and showed promising results using Lipinski's rule of five, suggesting that the molecule M3 could be a useful anti-angiogenesis agent. In conclusion, this study provides insights into the structure-activity relationship of VEGFR-2 inhibitors and identifies M3 as a potential new anti-angiogenesis drug. The methodology used in this study can be applied to other similar drug targets to discover new and potent inhibitors.Communicated by Ramaswamy H. Sarma.

Exploring azomethine ylides reactivity with acrolein through cycloaddition reaction and computational antiviral activity assessment against hepatitis C virus.

CONTEXT: The regioselectivity and diastereoselectivity of the 1,3-dipolar cycloaddition reaction between azomethine ylides and acrolein were investigated. The DFT studies revealed that the favored pathway leads to the formation of cis-cycloadduct pyrrolidine and these computational findings align with experimental observations. The cis-cycloadduct pyrrolidine product serves as an advanced intermediate in the synthesis of a hepatitis C virus inhibitor. For this, the antiviral activity of cis-cycloadduct pyrrolidine against cyclophilin A, the co-factor responsible for hepatitis C virus, was also evaluated through molecular docking simulations which revealed intriguing interactions and a high C-score, which were further confirmed by molecular dynamics simulations, demonstrating stability over a 100-ns simulation period. Furthermore, the cis-cycloadduct pyrrolidine exhibits favorable drug-like properties and a better ADMET profile compared to hepatitis C virus inhibitor. METHODS: Chemical reactivity studies were performed using DFT method by the functional B3LYP at 6-31G (d, p) computational level by GAUSSIAN 16 program. Frontal molecular orbitals theory used to investigate HOMO/LUMO interactions between azomethine ylides and acrolein. Findings of this approach were confirmed by global reactivity indices and electron displacement was investigated based on Fukui functions. Furthermore, the activation energies were determined after frequency calculations using TS Berny algorithm and transition states were confirmed by the presence of a single imaginary frequency. Moreover, antiviral activity of cis-cycloadduct was explored through molecular docking using Surflex-Dock suite SYBYL X 2.0, and molecular dynamics simulation using GROMACS program. Finally, drug-like properties were investigated with SwissADME and ADMETlab 2.0.

Molecular toxicity of nitrobenzene derivatives to tetrahymena pyriformis based on SMILES descriptors using Monte Carlo, docking, and MD simulations.

It is challenging to model the toxicity of nitroaromatic compounds due to limited experimental data. Nitrobenzene derivatives are commonly used in industry and can lead to environmental contamination. Extensive research, including several QSPR studies, has been conducted to understand their toxicity. Predictive QSPR models can help improve chemical safety, but their limitations must be considered, and the molecular factors affecting toxicity should be carefully investigated. The latest QSPR methods, molecular modeling techniques, machine learning algorithms, and computational chemistry tools are essential for developing accurate and robust models. In this work, we used these methods to study a series of fifty compounds derived from nitrobenzene. The Monte Carlo approach was used for QSPR modeling by applying the SMILES molecular structure representation and optimal molecular descriptors. The correlation ideality index (CII) and correlation contradiction index (CCI) were further introduced as validation parameters to estimate the developed models' predictive ability. The statistical quality of the CII models was better than those without CII. The best QSPR model with the following statistical parameters (Split-3): (R2 = 0.968, CCC = 0.984, IIC = 0.861, CII = 0.979, Q2 = 0.954, QF12 = 0.946, QF22 = 0.938, QF32 = 0.947, Rm2 = 0.878, RMSE = 0.187, MAE = 0.151, FTraining = 390, FInvisible = 218, FCalibration = 240, RTest2 = 0.905) was selected to generate the studied promoters with increasing and decreasing activity.

In silico computational drug discovery: a Monte Carlo approach for developing a novel JAK3 inhibitors.

Inhibition of Janus kinase 3 (JAK3), a member of the JAK family of tyrosine kinases, remains an essential area of research for developing treatments for autoimmune diseases, particularly cancer and rheumatoid arthritis. The recent discovery of a new JAK3 protein, PDB ID: 4Z16, offers exciting possibilities for developing inhibitors capable of forming a covalent bond with the Cys909 residue, thereby contributing to JAK3 inhibition. A powerful prediction model was constructed and validated using Monte Carlo methods, employing various internal and external techniques. This approach resulted in the prediction of eleven new molecules, which were subsequently filtered to identify six compounds exhibiting potent pIC50 values. These candidates were then subjected to ADMET analysis, molecular docking (including reversible-reversible docking with tofacitinib, an FDA-approved drug, and reversible-irreversible docking for the newly designed compounds), molecular dynamics (MD) analysis for 300 ns, and calculation of free binding energy. The results suggested that these compounds hold promise as JAK3 inhibitors. In summary, the new compounds have exhibited favorable outcomes compared to other compounds across various modeling approaches. The collective findings from these investigations provide valuable insights into the potential therapeutic applications of covalent JAK3 inhibitors, offering a promising direction for the development of novel treatments for autoimmune disorders.Communicated by Ramaswamy H. Sarma.

Profiling the structural determinants of pyrrolidine derivative as gelatinases (MMP-2 and MMP-9) inhibitors using in silico approaches.

Quantitative structure activity relationship (QSAR) studies on pyrrolidine derivatives have been established using CoMFA, CoMSIA, and Hologram QSAR analysis to estimate the values (pIC50) of gelatinase inhibitors. When the CoMFA cross-validation value, Q², was 0.625, the training set coefficient of determination, R² was 0.981. In CoMSIA, Q² was 0.749 and R² was 0.988. In the HQSAR, Q² was 0.84 and R² was 0.946. Visualization of these models was performed by contour maps showing favorable and unfavorable regions for activity, while visualization of HQSAR model was performed by a colored atomic contribution graph. Based on the results obtained of external validation, the CoMSIA model was statistically more significant and robust and was selected as the best model to predict new, more active inhibitors. To study the modes of interactions of the predicted compounds in the active site of MMP-2 and MMP-9, a simulation of molecular docking was realized. A combined study of MD simulations and calculation of free binding energy, were also carried out to validate the results obtained on the best predicted and most active compound in dataset and the compound NNGH as control compound. The results confirm the molecular docking results and indicate that the predicted ligands were stable in the binding site of MMP-2 and MMP-9.

Molecular modeling study of pyrrolidine derivatives as novel myeloid cell leukemia-1 inhibitors through combined 3D-QSAR, molecular docking, ADME/Tox and MD simulation techniques.

A series of pyrrolidine derivatives have been used to study the main structural requirements for designing novel Mcl-1 inhibitors. For this purpose, three models CoMSIA, CoMFA and HQSAR were generated using QSAR molecular modeling techniques. The statistical results of the CoMFA (Q2 = 0.689; R = 0.999; R2pred = 0.986), CoMSIA (Q2 = 0.614; R2 = 0.923; R2pred = 0.815) and HQSAR (Q2= 0.603; R2 = 0.662; R2pred = 0.743) models showed good stability and predictability. The results of the models were presented as contours and colored fragments indicating the favorable and unfavorable contribution to the inhibitory activity of Mcl-1. Based on the obtained results, four new compounds were designed with more potent predicted pIC50 inhibitory activity. The ADME/Tox results and the pharmacokinetic properties revealed that these four compounds are orally bioavailable and show good permeability. In addition the four compounds showing non-inhibitors of CYP3A4 and CYP2D6 with the exception of Pred03. At the level of toxicity profile, the compounds Pred01, Pred02 and Pred03 showed interesting results and showed no AMES toxicity, no hERG inhibition and no skin sensitization. Molecular docking results were used to uncover the mode of interaction between the ligand and key residues of protein binding site. Molecular docking results were supported by molecular simulation and binding free energy estimation (MMPBSA). These results demonstrate the stability of the analyzed compounds in the target protein binding site during a 100 ns trajectory. Finally, all these results create a strong lead to develop promising new Pyrrolidine-based inhibitors against Mcl-1.Communicated by Ramaswamy H. Sarma.