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.

In this study, we utilized machine learning techniques to identify potential inhibitors of the MERS-CoV 3CL protease. Among the models evaluated, the Random Forest (RF) algorithm exhibited the highest predictive performance, achieving an accuracy of 0.97, an ROC-AUC score of 0.98, and an F1-score of 0.98. Following model validation, we applied it to a dataset of 14,194 naturally occurring compounds from PubChem. The top-ranked compounds were subsequently subjected to molecular docking, which identified Perenniporide B, Phellifuropyranone A, and Terrestrol G as the most promising candidates, with binding energies of −9.17, −9.08, and −8.71 kcal/mol, respectively. These compounds formed strong interactions with key catalytic residues, suggesting significant inhibitory potential against the viral protease. Furthermore, molecular dynamics simulations confirmed their stability within the active site, reinforcing their viability as antiviral agents. This study demonstrates the effectiveness of integrating machine learning with molecular modeling to accelerate the discovery of therapeutic candidates against emerging viral threats.

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.

Artemisia annua is well known for its antimalarial properties; however, its diverse phytochemicals have also attracted attention for their antiviral potential. In this study, an integrated in silico approach—combining molecular docking, ADMET profiling, and 200-ns molecular dynamics (MD) simulations coupled with MM/GBSA free-energy calculations—was applied to evaluate A. annua–derived compounds against the proteases of HSV-2, MERS-CoV, and SARS-CoV-2. Among the screened metabolites, Chrysosplenol D (Mol007395) was identified as a promising candidate based on a combined assessment of docking performance, favorable drug-likeness (full compliance with Lipinski’s rule of five), and the highest quantitative estimate of drug-likeness (QED = 0.608). While PASS predictions suggested potential antiviral activity, these results were considered supportive and remain computational in nature. MD simulations and MM/GBSA analyses demonstrated that Chrysosplenol D forms a strong and dynamically stable complex with the HSV-2 protease, driven primarily by favorable van der Waals and electrostatic interactions. In contrast, its interaction with the MERS-CoV 3CLpro was characterized by a more flexible yet moderately stable binding mode, whereas the SARS-CoV-2 protease complex exhibited weak and unstable binding behavior. Overall, these findings highlight Chrysosplenol D as a selective and energetically favorable in silico inhibitor of the HSV-2 protease, supporting its potential as a lead scaffold for further experimental validation and structure–activity optimization toward the development of anti-HSV-2 antiviral agents.

Human immunodeficiency viru (HIV) is the causative agent of acquired immunodeficiency syndrome (AIDS), a disease that severely weakens the immune system and makes patients more susceptible to infections. Although there is no definitive cure for HIV, advances in drug development offer promising prospects. In this study, we targeted HIV-1 reverse transcriptase by performing virtual screening (VS) to identify novel candidate compounds. From a database of compounds similar to the inhibitor thymidine-5'-triphosphate (TTP), three compounds (CID441663, CID123650073, and CID123789980) were selected for their docking scores, which outperformed those of the reference compound TTP (-6.2302 kcal/mol). These compounds were then subjected to ADMET, PASS, and DFT analyses. Interestingly, all three ligands showed a broad spectrum of predicted antiviral activity, including targets related to human herpes virus and HIV. Specifically, while TTP primarily targets HIV-1 reverse transcriptase, the top three ligands were predicted to target HIV-1 integrase, with CID441663 and CID123789980 displaying higher confidence in this target compared to CID123650073. These findings suggest that the candidate ligands should undergo further in vitro validation to determine their precise roles as inhibitors or antagonists, and to confirm their selective targeting of HIV-related proteins.