Cancer remains a major global health challenge, emphasizing the need for new and effective therapies. This study investigates the anticancer potential of bioactive compounds from rosemary (Rosmarinus officinalis) using an integrative network pharmacology and computational approach. Twelve phytochemicals with favorable pharmacological profiles, optimal pharmacokinetics, and acceptable toxicological properties were evaluated, revealing 178 putative cancer-related targets. Protein–protein interaction (PPI) analysis highlighted ten key genes—EGFR, ESR1, HIF1A, HSP90AA1, MAPK1, BCL2, STAT3, TP53, CASP3, and SRC—implicated in the progression of various cancers, including breast, colorectal, liver, and lung tumors. Functional enrichment analysis demonstrated their involvement in multiple cancer-associated pathways. Among these, HSP90AA1 emerged as a critical target. Molecular docking revealed Rosmanol, Chlorogenic acid, and Carnosol as the most promising HSP90AA1 binders with strong predicted affinities. ADMET profiling confirmed their excellent drug-likeness and safety profiles, while molecular dynamics simulations validated the stability of the compound–protein complexes, further supporting their potential as HSP90 inhibitors. These findings suggest that rosemary-derived compounds may represent valuable candidates for anticancer drug development, though experimental validation is required to confirm their therapeutic efficacy.

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski’s and Veber’s rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets. Communicated by Ramaswamy H. Sarma

The avian influenza virus, particularly the highly pathogenic H5N1 subtype, represents a significant public health threat due to its interspecies transmission potential and growing resistance to current antiviral therapies. To address this, the identification of novel and effective neuraminidase (NA) inhibitors is critical. In this study, an integrated in silico strategy was employed, beginning with the generation of an energy-optimized pharmacophore model (e-pharmacophore, ADDN) based on the reference inhibitor Zanamivir. A virtual screening of 47,781 natural compounds from the PubChem database was performed, followed by molecular docking validated through an enrichment assay. Promising hits were further evaluated via ADMET predictions, density functional theory (DFT) calculations to assess chemical reactivity, and molecular dynamics (MD) simulations to examine the stability of the ligand–protein complexes. Three lead compounds (C1: CID 102209473, C2: CID 85692821, and C3: CID 45379525) demonstrated strong binding affinity toward NA. Their ADMET profiles predicted favorable bioavailability and low toxicity. The DFT analyses indicated suitable chemical reactivity, particularly for C2 and C3. The MD simulations confirmed the structural stability of all three ligand–NA complexes, supported by robust and complementary intermolecular interactions. In contrast, Zanamivir exhibited limited hydrophobic interactions, compromising its binding stability within the active site. These findings offer a rational foundation for further experimental validation and the development of next-generation NA inhibitors derived from natural sources.

This study aims to identify N-aryl-benzimidazolone analogs as potential inhibitors of the HSP90 protein, which is involved in various diseases. For this, we used computational techniques such as pharmacophoric modeling, virtual screening, in silico ADMET prediction, and molecular dynamics simulations. A target-based pharmacophore model (ADDRR) was developed from the MEY ligand to identify the main binding features. This model was used to screen approximately 30,994 similar compounds, leading to the identification of 3019 candidates. Among these, five compounds (L1, L2, L3, L4, and L5) showed strong binding affinity, with docking scores lower than the reference ligand MEY (−7.94 kcal/mol). The ADMET properties of these compounds were favorable, confirming their potential as drug candidates. The two top-performing compounds in the docking studies demonstrated high stability in dynamics studies, the results demonstrated remarkable stability of the ligand−protein complexes, as evidenced by favorable values of metrics such as RMSD, RMSF, Rg, and SASA. These findings provide a promising foundation for further experimental validation and the potential development of effective HSP90 inhibitors.