Glyphosate is indeed one of the most extensively used herbicides worldwide, valued for its effectiveness in controlling weeds and increasing crop yields. However, its widespread use has prompted ongoing debates and research regarding its environmental and ecological impacts. Concerns include potential effects on non-target plant species, soil health, aquatic ecosystems, and overall biodiversity. Some studies suggest that glyphosate may influence soil microbial communities and have unintended consequences on beneficial insects and wildlife. As a result, many countries and organizations are reevaluating regulations and promoting integrated weed management practices to balance agricultural productivity with ecosystem sustainability.

Benzimidazole continues to be an intriguing scaffold in recent drug discovery, owing to its broad spectrum of pharmacological effects. In recent years, a variety of its derivatives, which included chalcone imines, hydrazones, and thiosemicarbazones, were actively investigated for their antitumor potential. In the search for new agents capable of treating kidney cancer, an analysis of a small series of 2-substituted benzimidazoles (45) using 3D-QSAR modelling was performed to determine the antiproliferative activities against cancer cell lines A-498. The biological activity was sufficient to establish a meaningful structure–activity relationship, providing a foundation for the design of more potent compounds. The activity-favouring and activity-disfavoring structural regions were clearly revealed using contour maps generated by the models. The CoMSIA/SHD model was one of the best developed, and its high statistical robustness (q2 = 0.751) and predictive power (R2 pred = 0.924) indicated its reliability. We designed five new derivatives of benzimidazole based on the QSAR results, which demonstrated potent inhibitory potential. Molecular docking studies were performed in order to investigate in detail their interaction modes with the aromatic receptor, and stable binding conformations at the active site have been found. The in silico pharmacokinetic studies suggested that these compounds have a favourable ADMET and bioavailability profile, reinforcing their suitability for in vitro testing. Two leads, L15 and L22, with better PKs properties and high-predicted activities, were subjected to a 100-ns MD simulation in complex with the aromatase target to investigate their stability. We also conducted a retrosynthetic analysis for L15 and L22, suggesting potential synthetic routes for experimental validation. Overall, these findings suggest that benzimidazole analogues could be promising candidates for treating RCC and possibly for blocking aromatase.

The investigation of 1,2,4-triazole-chalcone has sparked immense interest due to their promising biological activities. These compounds, labeled 10C-10S, were synthesized and characterized by Jinjing et al., specifically focusing on their potential applications in biological settings, particularly their antiproliferative properties. Strategic exploration by computational chemistry techniques such as DFT calculations, molecular docking, and molecular dynamics with empirical findings proved pivotal in unraveling the multifaceted properties of these organic molecules. Additionally, molecular docking studies were conducted to elucidate the antiproliferative effects and analyze the potential binding modes of the compounds with specific amino acid residues in proteins. Rigorous comparisons between theoretical and experimental results yielded comprehensive insights into the properties of these compounds. We chose two molecules, C (the most active) and E (the least active), which have affinities of -7.689 and -7.526 kcal/mol, respectively, to test how stable they are with the EGFR receptor (PDB entry code: 6Z4B). Molecular dynamics simulations over 100 ns revealed more stable energies, with ΔG_Bind = -25.135 Kcal/mol and ΔG_Bind_vdW = -30.644 Kcal/mol.

Diabetes mellitus is a serious health disease that affects people all over the world. The number of persons identified with diabetes mellitus rises each year. α -Glucosidase is a digestive enzyme used to control diabetes mellitus. The searching for new potent α-glucosidase inhibitors capable of delaying carbohydrate digestion in the human body is an important strategy towards control of diabetes mellitus. In this work, a series of quinoline-based Schiff base derivatives already identified as α-glucosidase inhibitory activity was studied by using 2D/3D-QSAR approach. The best HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA models were constructed using thirteen molecules in the training set, resulting in favorable values of Q2 (0.834 and 0.607), and high values of R2 (0.985 and 0.912), respectively. The generated HQSAR/A-B-C-H-Ch-DA and CoMSIA/SEDA contour plots were precious for designing and enhancing the α-glucosidase inhibitory activity of quinoline-based Schiff base molecules. Considering these results, two novel α-glucosidase compounds were designed to possess significant activity. The newly suggested molecules showed good outcomes in the preliminary in silico ADME/Tox evaluations. Molecular docking results revealed that the new designed inhibitors have a good stability in the active pocket of the studied receptor compared to voglibose, clinically used as an α-glucosidase inhibitor. MD simulation and MM-GBSA results confirmed the molecular docking outcomes. Finally, DFT analysis was useful in determining the most electrophilic and nucleophilic centers of the two designed α-glucosidase inhibitors.

While the number of AIDS-related deaths continues to rise, efforts have been made to transform the disease into a manageable chronic condition. HIV protease inhibitors have become central to combination therapy. As a result, these inhibitors have become a major focus of anti-HIV drug development. This research takes a data-driven approach to drug development through the use of quantitative structure-activity relationship (QSAR) analysis. A dataset of 450 anti-HIV drugs was used to construct and validate models. Using extensive validation methods and various machine learning algorithms, the results clearly showed that the "ET" regression outperformed the other models (“XGB”, “LGBM”, “DT”, “RF”, “GB”, “Bag”, and “HGB”) in terms of goodness of fit, predictivity, generalizability, and model robustness. Promising compounds were subjected to molecular docking and molecular dynamics simulation, resulting in drugs with favourable pharmacokinetic and pharmacodynamic properties that consistently interact with the therapeutic target.