KRAS-G12D has long been regarded as an intractable therapeutic target due to the flat binding pocket and its strong affinity for GTP/GDP. Proteolysis-targeting chimera (PROTAC) is a revolutionary drug discovery strategy that, by virtue of its unique pharmacological mode of action, provides more options for targeting undruggable targets. In this study, we designed and synthesized 20 novel KRAS G12D PROTACs based on the MRTX1133 derivative. Through systematic exploration of linker structure-activity relationship and multi-cell line screening, compound VI-1 exhibited significant KRAS G12D degradation activity in PANC-0203 cells, achieving 69% effective degradation at 10 μM. Notably, the preferred compounds exhibited significant selectivity for other KRAS mutations and normal cells. This work provides an important lead compound for developing highly selective KRAS G12D PROTACs and warrants further exploration in the context of drug-likeness optimization.
Lili Jiang, Wenyan Yang, Yanqing Liu et al.· European Journal of Pharmace...· 0 citations
Aurora kinase A (AURKA) is a pivotal driver of malignant progression and poor prognosis in triple-negative breast cancer (TNBC). In this study, we developed a cascaded AI-driven virtual screening pipeline, integrating sequence-based affinity prediction (PSICHIC), equivariant deep learning docking (KarmaDock), and geometric rescoring (DeepDock) to identify novel AURKA inhibitor candidates. From an in-house 160,000-compound screening library assembled from commercially available collections, three leads (compounds 3, 5, and 8) were selected and subsequently validated via HTRF biochemical assays, exhibiting potent enzymatic inhibition with IC50 values of 157 nM, 21.64 nM, and 46.03 nM, respectively. Cell-based assays demonstrated that compound 3 produced stronger short-term cell-growth inhibition in MDA-MB-231 (TNBC) cells compared to clinical benchmarks MLN8237 and CCT241736, whereas compounds 3 and 5 showed cell-growth inhibition in NIH/3T3 cells within the same concentration range as the reference inhibitors. Triplicate 500 ns molecular dynamics simulations supported stable binding modes of the identified leads in the AURKA binding pocket. Additional computational analyses further provided supportive information for subsequent lead optimization. This study provides a transparent and open-source workflow for AI-assisted identification of AURKA-active chemotypes.