Oussama Abchir

Background/Objectives: Chromium contamination represents a major environmental challenge due to its detrimental effects on plant growth and agricultural productivity. Since dichromate uptake in plants occurs mainly through sulfate and phosphate transporters, identifying natural compounds capable of competitively inhibiting these transport pathways may provide an eco-friendly strategy for reducing chromium accumulation. This study aimed to investigate the inhibitory potential of phytochemicals from Djiboutian medicinal plants against sulfate and phosphate transporters using an integrated computational approach. Methods: 49 phytochemicals identified by GC–MS from ten Djiboutian medicinal plants were screened against the sulfate transporter (7LHV) and phosphate transporter (7SP5) using molecular docking. Binding interactions were compared with sulfate, phosphate, and dichromate ions to evaluate potential competitive inhibition. The most promising compounds were further assessed through ADMET prediction and 100 ns molecular dynamics simulations to evaluate their pharmacokinetic properties and complex stability. Results: Molecular docking revealed binding energies ranging from −7.04 to −2.91 kcal/mol for 7LHV and from −6.50 to −0.62 kcal/mol for 7SP5, indicating variable binding affinities among the screened phytochemicals. Several compounds exhibited favorable interactions with key amino acid residues involved in anion transport, suggesting their potential to compete with dichromate uptake. ADMET analysis identified multiple compounds with favorable toxicity and drug-likeness profiles. Among them, cyclohexanepropanoic acid from Aloe djiboutiensis demonstrated the strongest binding affinity toward both transporters. Molecular dynamics simulations confirmed the structural stability of the protein–ligand complexes throughout the 100 ns simulation. Conclusions: This study identifies naturally occurring phytochemicals, particularly cyclohexanepropanoic acid, as promising competitive inhibitors of dichromate transport in plants. These findings provide a theoretical foundation for developing sustainable phytochemical-based strategies to mitigate chromium accumulation in crops and support future experimental validation.

Diabetes mellitus remains a major metabolic disorder requiring the development of new and effective α-glucosidase inhibitors. The present study aimed to identify, design, and optimize novel 3-amino-2,4-diarylbenzo[4,5]imidazo[1,2-α]pyrimidine derivatives with promising inhibitory activity against the α-glucosidase enzyme using a comprehensive in silico strategy. Approximately 300 molecular descriptors were calculated to characterize a dataset of 32 compounds (Peytam et al.) and to investigate the structural factors governing their biological activity. Based on these descriptors, a multiple linear regression model was developed to predict the inhibitory activities of the compounds against alpha-glucosidase. The developed model demonstrated satisfactory predictive performance and was internally and externally validated to ensure its accuracy, robustness, and reproducibility. In addition, the applicability domain analysis confirmed the reliability of the predictions. Using the validated QSAR model, seven new derivatives were designed with predicted pIC₅₀ values exceeding the maximum activity of the parent compounds. The leverage analysis demonstrated that all newly designed compounds were located within the applicability domain of the model, supporting the reliability of the predictions. To further evaluate their inhibitory potential, molecular docking studies were performed to investigate the interactions between the designed compounds and the α-glucosidase active site. The docking results revealed favorable binding interactions comparable to those reported for known α-glucosidase inhibitors. Furthermore, ADMET analysis indicated generally favorable pharmacokinetic properties, although potential CYP3A4 inhibition-related pharmacokinetic risks were identified and discussed. Molecular dynamics simulations, including replicated runs and MM/GBSA binding free energy calculations, confirmed the stability of the most promising protein–ligand complexes throughout the simulation period. In conclusion, this study proposes a robust and integrated computational workflow combining descriptor generation, QSAR modeling, applicability domain analysis, molecular docking, ADMET prediction, and molecular dynamics simulations for the rational design of potential α-glucosidase inhibitors. The findings highlight the therapeutic potential of the designed derivatives and provide a valuable in silico framework for the future development of antidiabetic agents.

Background/Objectives: Diabetes mellitus is a serious global disease characterized by chronic hyperglycemia, resulting from defects in insulin secretion, insulin action, or both. It represents a major health concern affecting millions of people worldwide. This condition can lead to severe complications significantly affecting patients’ quality of life. Due to the limitations and side effects of current therapies, the search for safer and more effective antidiabetic agents, particularly from natural sources, has gained considerable attention. This study investigates the antidiabetic potential of seaweed-derived compounds through structure-based virtual screening targeting α-glucosidase. Methods: A library of compounds derived from the Seaweed Metabolite Database was subjected to a hierarchical molecular docking protocol against α-glucosidase. Extra Precision (XP) docking was employed to identify the top-ranked ligands based on their binding affinities. Drug-likeness was assessed according to Lipinski’s Rule of Five, followed by pharmacokinetic and toxicity predictions to evaluate ADMET properties. Density Functional Theory (DFT) calculations were performed to analyze the electronic properties and chemical reactivity of the selected compounds. Furthermore, molecular dynamics simulations were carried out to examine the stability and dynamic behavior of the ligand–enzyme complexes. Results: Following XP docking and ADMET prediction, four promising compounds were selected: Colensolide A, Rhodomelol, Callophycin A, and 7-(2,3-dibromo-4,5-dihydroxybenzyl)-3,7-dihydro-1H-purine-2,6-dione. Molecular dynamics simulations further confirmed the structural stability and strong binding interactions of these compounds within the α-glucosidase active site. Conclusions: This investigation demonstrated the important role of seaweed-derived compounds in inhibiting α-glucosidase activity. Further experimental validation is warranted to confirm their biological activity and therapeutic potential.

Background/Objectives: RNA polymerase (NS5B), serves as a crucial target for pharmaceutical interventions aimed at combating the hepatitis C virus (HCV), which poses significant health challenges worldwide. The present research endeavors to explore and implement a variety of advanced molecular modeling techniques that aim to create and identify innovative and highly effective inhibitors that specifically target the RNA polymerase enzyme. Methods: In this study, a QSAR investigation was carried out on a set of thirty-eight isothiazole derivatives targeting NS5B inhibition and thus hepatitis C virus (HCV) treatment. The research methodology made use of various statistical techniques including multiple linear regression (MLR) and artificial neural networks (ANNs) to develop satisfactory models in terms of internal and external validation parameters, indicating their reliability in predicting the activity of new inhibitors. Accordingly, a series of potent NS5B inhibitors is designed, and their inhibitory potential is confirmed through molecular docking simulations. Results: These simulations showed that the interactions between these inhibitors and the active site 221 binding pocket of the NS5B protein are hydrophobic and hydrogen bond interactions, as well as carbon–hydrogen bonds and electrostatic interactions. Additionally, these newly formulated compounds displayed favorable ADMET characteristics, with molecular dynamics investigations revealing a stable energetic state and dynamic equilibrium. Conclusions: Our work highlights the importance of NS5B inhibition for the treatment of HCV.

Artificial neural networks (ANN) are very useful for predicting biological activities in QSAR studies. ANNs allow the study of complex and nonlinear SAR. We use ANN and MLR methods to generate QSAR models for Calcium Channel Blockers activity of a series of 1,4-dihydropyridines. Molecular descriptors were calculated by using DFT method at the B3LYP/6-31G+ (d, p) level. Statistical analyzes show that the predicted values of the activities are in excellent agreement with the experimental results. Molecular docking studies have been performed, in order to re-estimate the activity of molecules as CCBs by analyzing their binding energies and mutual interaction types.

Background: Angiogenesis is a crucial process in the growth and proliferation of cancer, enabling tumor growth through the formation of new vasculature and the supply of nutrients and oxygen to growing malignant cells. This disease-promoting process can be targeted through the inhibition of tyrosine kinase enzymes. Objectives: The objective of this study is to evaluate the anticancer potential of various Moroccan plants from different regions. While these plants have a rich history of traditional medicinal use, they have not been extensively investigated as anticancer therapies. Methods: This study employed a multifaceted approach to evaluate the anticancer potential of various Moroccan plants. Receptor–ligand docking and virtual screening were used to assess the binding affinity of phytocompounds to the EGFR and VEGFR2 receptors. Additionally, predictive pharmacokinetic analyses were conducted to evaluate the ADMET properties of the selected compounds, followed by molecular dynamics simulations to analyze the stability of the receptor–ligand complexes. Results: In our research, we identified three notable active compounds—catechin, 4-O-glucoside ferulic acid, and 3-glucoside resveratrol—in the Moroccan plant Ajuga iva L. These findings suggest that Ajuga iva L. may possess significant potential for cancer inhibition. Conclusions: This research highlights the potential of the Moroccan plant Ajuga iva L. as a source of active compounds with significant anticancer properties. Further investigation is essential to validate these findings and explore new therapeutic avenues based on these traditional resources.

Mammalian target of rapamycin complex 1 (mTORC1) is an important and promising alternative biological target for the treatment of different types of cancer including breast, lung and renal cell carcinoma. This study contributed to the development of mathematical models highlighting the quantitative structure-activity relationship of a series of piperazine derivatives reported as mTORC1 inhibitors. Various molecular descriptors were calculated using Gaussian 09, Chemsketch, and ChemOffice software. The density funcional theory (DFT) method at the level B3LYP/6-31G+(d, p) was applied to determine the structural, electronic and energetic parameters associated with the studied molecules. The predictive ability of the built models, which is obtained by two methods (MLR and MNLR), showed that the built models are statistically significant. The QSAR modeling results revealed that the six molecular descriptors of lowest unoccupied molecular orbital energy (ELUMO), electrophilicity index (ω), molar refractivity (MR), aqueous solubility (Log S), topological polar surface area (PSA), and refractive index (n) significantly correlated to the biological inhibitory activity of piperazine derivatives. Using QSAR models and in silico pharmacokinetic profiles predictions, five new candidate compounds are selected as potential inhibitors against cancer.

Candida albicans and Aspergillus fumigatus are recognized as significant fungal pathogens, responsible for various human infections. The rapid emergence of drug-resistant strains among these fungi requires the identification and development of innovative antifungal therapies. We undertook a comprehensive screening of 297 naturally occurring compounds to address this challenge. Using computational docking techniques, we systematically analyzed the binding affinity of each compound to key proteins from Candida albicans (PDB ID: 1EAG) and Aspergillus fumigatus (PDB ID: 3DJE). This rigorous in silico examination aimed to unveil compounds that could potentially inhibit the activity of these fungal infections. This was followed by an ADMET analysis of the top-ranked compound, providing valuable insights into the pharmacokinetic properties and potential toxicological profiles. To further validate our findings, the molecular reactivity and stability were computed using the DFT calculation and molecular dynamics simulation, providing a deeper understanding of the stability and behavior of the top-ranking compounds in a biological environment. The outcomes of our study identified a subset of natural compounds that, based on our analysis, demonstrate notable potential as antifungal candidates. With further experimental validation, these compounds could pave the way for new therapeutic strategies against drug-resistant fungal pathogens.

Acetylcholinesterase (AChE) is one of the main drug targets for treating Alzheimer’s disease. This current study relies on multiple molecular modeling approaches to develop new potent inhibitors of AChE. We explored a 2D QSAR study using the statistical method of multiple linear regression based on a set of substituted 5-phenyl-1,3,4-oxadiazole and N-benzylpiperidine analogs, which were recently synthesized and proved their inhibitory activities against acetylcholinesterase (AChE). The molecular descriptors, polar surface area, dipole moment, and molecular weight are the key structural properties governing AChE inhibition activity. The MLR model was selected based on its statistical parameters: R² = 0.701, R²test = 0.76, Q²CV = 0.638, and RMSE = 0.336, demonstrating its predictive reliability. Randomization tests, VIF tests, and applicability domain tests were adopted to verify the model’s robustness. As a result, 11 new molecules were designed with higher anti-Alzheimer’s activities than the model molecule. We demonstrated their improved pharmacokinetic properties through an in silico ADMET study. A molecular docking study was conducted to explore their AChE inhibition mechanisms and binding affinities in the active site. The binding scores of compounds M1, M2, and M6 were (−12.6 kcal/mol), (−13 kcal/mol), and (−12.4 kcal/mol), respectively, which are higher than the standard inhibitor Donepezil with a binding score of (−10.8 kcal/mol). Molecular dynamics simulations over 100 ns were used to validate the molecular docking results, indicating that compounds M1 and M2 remain stable in the active site, confirming their potential as promising anti-AChE inhibitors.