Discovery of Novel AURKA Inhibitors for Triple-Negative Breast Cancer (TNBC) Therapy via a Hybrid Virtual Screening Pipeline, Biological Evaluation and Molecular Dynamics Simulation

Virtual screening and MD simulation-guided discovery of CDK12-IN-3, a potent ALK inhibitor suppressing neuroblastoma growth in vitro and in vivo

Neuroblastoma (NB) is the most common extracranial malignant solid tumor in children, and aberrant activation of anaplastic lymphoma kinase (ALK)—driven primarily by hotspot mutations F1174L and R1275Q—represents a core oncogenic driver of high-risk and relapsed NB. Clinical ALK inhibitors are frequently hampered by acquired drug resistance and off-target toxicity, highlighting an unmet need for novel selective ALK inhibitors for NB treatment. Here, we employed a drug repurposing strategy integrating virtual screening, multi-dimensional molecular dynamics (MD) simulations, steered MD, and umbrella sampling to screen 7,322 compounds for novel ALK inhibitory activity, followed by systematic in vitro and in vivo validation. We identified CDK12-IN-3 (C18) as a high-affinity ligand for wild-type ALK and both NB-associated ALK mutants (F1174L, R1275Q), with favorable binding selectivity over 10 homologous tyrosine kinases. In vitro , CDK12-IN-3 exerted nanomolar-level antiproliferative, anti-clonogenic, and anti-migratory effects across three genetically distinct NB cell lines (SH-SY5Y, SK-N-BE (2), IMR-32), while showing no significant cytotoxicity in ALK-negative HEK293T normal cells even at 20 μM, demonstrating a wide in vitro safety window. Mechanistic studies confirmed that CDK12-IN-3 directly bound ALK (KD = 5.33 μM by surface plasmon resonance), downregulated ALK expression, suppressed the downstream PI3K/AKT/mTOR signaling pathway, and induced NB cell apoptosis. In vivo , oral administration of CDK12-IN-3 (50 mg/kg) significantly suppressed NB xenograft tumor growth in nude mice with no observable systemic toxicity. Collectively, our findings validate CDK12-IN-3 as a novel selective ALK inhibitor with potent anti-NB activity, providing a promising lead compound and an efficient dynamic simulation-guided drug repurposing paradigm for overcoming ALK inhibitor resistance in high-risk NB.

AI-Driven Computational Design of Peptide-Based WWP1 Inhibitors as Promising Therapeutic Agents Against Breast Cancer, Including Triple-Negative Subtype

Machine Learning-Driven Discovery of Novel HER2 Inhibitors Through Integrated Virtual Screening and Molecular Dynamics Simulations

Background: HER2 is a key oncogenic gene in breast cancer, involved in tumor progression, metastasis, and therapeutic resistance. This study aimed to find new HER2 inhibitors using a hybrid of machine learning (ML) and structure-based virtual screening (VS), combined with molecular dynamics (MD) simulations on various scaffolds. Methods: Four supervised molecular fingerprint classification models were trained on a dataset of 10,000 validated compounds from ChEMBL. Random Forest was the top model for screening a large compound library. Selected compounds underwent molecular docking in the HER2 ATP binding site, ADMET, drug likeness, toxicity analysis, and 200 ns MD simulations. Methods like PCA, FEL, hydrogen-bond analysis, DCCM, RDF, salt-bridge analysis, and MM/PBSA were used to assess binding stability. Results: Virtual screening identified three compounds, CHMEBL193865 (Lead-1), CHMEBL46740 (Lead-2), and CHMEBL151318 (Lead-3)—with better binding affinity and interaction profiles than the reference inhibitor. MD simulations showed stable protein–ligand complexes with RMSD values of 2.32–2.76 Å. Among these, Lead-2 was the most structurally stable, and Lead-1 had the most favorable binding free energy. All three compounds showed good drug likeness, ADMET properties, and low predicted toxicity. Conclusions: These findings support further in vitro and in vivo testing for developing new therapeutics against HER2-overexpressing breast cancer, highlighting two scaffolds with promising lead optimization potential.

P1.062. Discovery of Novel Dual-Target Inhibitors for EGFR and PIK3CA From Natural Products via Machine Learning and Molecular Simulation

The natural product compounds CNP0456830 and CNP0467494 exhibited the lowest binding free energies for both EGFR and PIK3CA, identifying them as the most promising dual-target inhibitors.

A multistage computational pipeline integrating ligand-based and structure-based screening with ADMET evaluation, molecular dynamic simulation and free energy calculations for the discovery of potential TNIK inhibitors.

This LBVS-SBVS-ADMET-MD pipeline effectively identified three promising TNIK inhibitors, providing a solid foundation for future experimental validation and potential development of targeted therapies for Wnt-driven malignancies.

Discovery of potential TMPRSS2 inhibitors using integrated computational and experimental screening.

In prostate cancer (PCa), the TMPRSS2-ERG fusion gene drives aberrant activation of androgen receptor (AR) signaling, while TMPRSS2 protease activity contributes to remodeling the tumor microenvironment. Targeting TMPRSS2 can suppress cancer metastasis and represents a potential therapeutic strategy for castration-resistant prostate cancer (CRPC). This study aimed to screen and identify promising TMPRSS2-binding lead candidates via integrated virtual screening and experimental validation. Screening of the ChemDiv, ChemBridge, and TargetMol compound libraries yielded 53 hit compounds. Surface plasmon resonance (SPR) assays were subsequently performed to evaluate binding affinities. Six candidate compounds (15, 26, 29, 31, 41, and 42) exhibited higher affinity for TMPRSS2 compared to the reference inhibitor Nafamostat (KD = 6.09 × 10-5 M). Among them, compound 26 showed the highest affinity (KD = 3.88 × 10-6 M). Subsequent in vitro inhibition assays using LNCaP clone FGC cells demonstrated that compound 26 possessed the strongest anti-proliferative activity (IC50 = 10.95 μM), indicating its therapeutic potential against PCa. Confocal microscopy showed apparent reduced TMPRSS2 staining after 24 h compound 26 treatment, implying potential TMPRSS2 modulation. Further mechanistic insights were gained through molecular dynamics (MD) simulations, alanine scanning mutagenesis, quantum mechanics/molecular mechanics (QM/MM) calculations, and dynamical cross-correlation matrix (DCCM) analysis, which revealed specific ligand-protein interactions in the compound 26-TMPRSS2 complex and reinforced the reliability of our findings. Collectively, these results identify compound 26 as a promising TMPRSS2-binding lead candidate, providing a solid foundation for further biochemical verification and structural optimization.