Targeting L858R/T790M and L858R/T790M/C797S mutant EGFR is a critical challenge in developing EGFR tyrosine kinase inhibitors to overcome drug resistance in non-small cell lung cancer (NSCLC). The discovery of next-generation EGFR tyrosine kinase inhibitors (TKIs) is therefore necessary. To this end, a series of furopyridine derivatives were evaluated for their EGFR-based inhibition and antiproliferative activities using computational and biological approaches. We found that several compounds derived from virtual screening based on a molecular docking and solvated interaction energy (SIE) method showed the potential to suppress wild-type and mutant EGFR. The most promising PD13 displayed strong inhibitory activity against wild-type (IC₅₀ of 11.64 ± 1.30 nM), L858R/T790M (IC₅₀ of 10.51 ± 0.71 nM), which are more significant than known drugs. In addition, PD13 revealed a potent cytotoxic effect on A549 and H1975 cell lines with IC₅₀ values of 18.09 ± 1.57 and 33.87 ± 0.86 µM, respectively. The 500-ns MD simulations indicated that PD13 formed a hydrogen bond with Met793 at the hinge region, thus creating excellent EGFR inhibitory activity. Moreover, the binding of PD13 in the hinge region of EGFR was the major determining factor in stabilizing the interactions via hydrogen bonds and van der Waals (vdW). Altogether, PD13 is a promising novel EGFR inhibitor that could be further clinically developed as fourth-generation EGFR-TKIs.

Janus kinase 2 (JAK2) is a key mediator of oncogenic signaling and a promising therapeutic target in cervical cancer. This study employed a combination of in silico and in vitro approach to discover sesquiterpene lactone (SL) derivatives with JAK2 inhibitory activity. Molecular docking of forty SL derivatives, followed by drug-likeness and toxicity prediction, led to the selection of six candidates for synthesis and biological evaluation. Among these, SL10 (12.7 nM) and SL35 (21.7 nM) demonstrated potent JAK2 inhibition and exhibited selective cytotoxicity toward HeLa cervical cancer cells, outperforming ruxolitinib. Flow cytometry confirmed apoptosis induction and ROS elevation, suggesting ROS-mediated cytotoxic mechanisms. The 1 µs MD simulations demonstrated that both hydrogen bonding and hydrophobic interactions are critical determinants in stabilizing potent SLs–JAK2 complexes. These findings support SL10 and SL35 as promising scaffolds for further development of JAK2-targeted therapies in cervical cancer.

Liver cancer remains a significant global health burden, requiring the development of precise nucleic acid delivery systems. Lipid nanoparticles (LNPs) are leading candidates; however, their efficiency is governed by the pK_a of ionizable lipids, which dictates nanoparticle stability and endosomal escape. In this study, we employed a machine learning–driven quantitative structure–activity relationship framework to predict the pK_a of ionizable lipids derived from the DLin–KC2–DMA scaffold. Utilizing a dataset of 56 compounds, we compared Random Forest, Artificial Neural Network, and Extreme Gradient Boosting (XGB) models integrated with Permutation Importance (PI) for feature selection. The optimized PI–XGB model exhibited exceptional predictive accuracy (R² = 0.970, R²_CV = 0.901, RMSE_test = 0.115) and robust generalization confirmed via external validation (RMSE_ext. = 0.313). Mechanistic insights derived from SHapley Additive exPlanation analysis identified charge distribution, molecular topology, and polarity as critical determinants of lipid ionization. These results demonstrate the power of interpretable machine learning in elucidating molecular structure–property relationships, offering a robust computational strategy for the rational design of next–generation ionizable lipids to optimize LNP–mediated gene therapy for liver cancer.