SHP2 belongs to a cytoplasmic non-receptor protein tyrosine phosphatase class. It plays a critical role in the development of various cancers, such as gastric cancer, leukemia, and breast cancer. Thus, SHP2 has gained the interest of researchers as a potential target for inhibiting tumor cell proliferation in SHP2-dependent cancers. This study employed pharmacophore-based virtual screening, molecular docking, molecular dynamic (MD) simulations, MM/PBSA, and principal component analysis (PCA), followed by ADME prediction. We selected three potential hits from a collective database of more than one million chemical compounds. The stability of these selected hit–protein complexes was analyzed using 500 ns MD simulations and binding free energy calculations. The identified hits Lig_1, Lig_6, and Lig_14 demonstrated binding free energies of −161.49 kJ/mol, −151.28 kJ/mol, and −107.13 kJ/mol, respectively, compared to the reference molecule (SHP099) with a ΔG of −71.48 kJ/mol. Our results showed that the identified compounds could be used as promising candidates for selective SHP2 allosteric inhibition in cancer.

Head and neck cancer ranks as the sixth-most common malignancy worldwide, characterized by high mortality and recurrence rates. Research studies indicate that molecular diagnostics play a crucial role in the early detection and prognostic evaluation of these diseases. This study aimed to identify potential biomarkers for head and neck cancer and elucidate their interactions with miRNAs and possible therapeutic drugs. Four drivers, namely, FN1, IL1A, COL1A1, and MMP9, were identified using network biology and machine learning approaches. Gene set variation analysis (GSVA) showed that these genes were significantly involved in different biological processes and pathways, including coagulation, UV-response-down, apoptosis, NOTCH signaling, Wnt-beta catenin, and other signal pathways. The diagnostic value of these hub genes was validated using receiver operating characteristic (ROC) curves. The top interactive miRNAs, including miR-128-3p, miR-218-5p, miR-214-3p, miR-124-3p, miR-129-2-3p, and miR-1-3p, targeted the key genes. Furthermore, the interaction between the key genes and drugs was also identified. In summary, the key genes and miRNAs or drugs reported in this study might provide valuable information for potential biomarkers to increase the prognosis and diagnosis of head and neck cancer.

Background: Cyclin-dependent kinase 2 (CDK2) is a key regulator of cell cycle progression and an important therapeutic target in cancer treatment. This study aims to identify novel CDK2 inhibitors using an integrated computational approach combining machine learning and structure-based methods. Methods: A computational pipeline was developed incorporating Lipinski’s Rule of Five filtering, machine learning (ML)-based activity prediction, molecular docking, and molecular dynamics simulations (MDs). A dataset of CDK2 inhibitors with IC50 values was retrieved from ChEMBL, and molecular fingerprints were generated using PaDEL. A 5-fold stratified cross-validation approach was applied to train multiple classifiers, with the random forest model showing the best performance. Predicted active compounds from the InterBioScreen database were subjected to docking against CDK2 (PDB ID: 2FVD) using PyRx, followed by 100 ns MDS for stability analysis. Results: The random forest classifier achieved an AUC-ROC of 0.90 and an accuracy of 0.84. A total of 187 compounds were predicted as active. Among these, two compounds, STOCK4S-00019 and STOCK4S-00025, demonstrated docking scores comparable to the co-crystallized reference ligand. Molecular dynamics simulations confirmed stable binding, consistent interaction patterns, and favorable conformational behavior throughout the simulation period. Conclusions: The identified compounds, STOCK4S-00019 (hit1) and STOCK4S-00025 (hit2), show strong potential as CDK2 inhibitors. These findings support their further investigation through experimental validation and highlight the effectiveness of integrated computational approaches in anticancer drug discovery.