Background: Breast cancer (BC) is one of the most diagnosed malignancies and a leading cause of cancer-related mortality among women worldwide, thereby posing a substantial threat to women’s health worldwide. However, clinically robust diagnostic biomarkers with high sensitivity and specificity, as well as well-validated molecular targets for targeted therapy, remain limited. Methods: BC transcriptomic data from seven GEO datasets and the TCGA-BRCA cohort (n = 1231) were integrated for analysis. After batch-effect correction, candidate genes were screened through DEA, WGCNA, and PPI networks analysis. An ensemble machine learning (ML) framework incorporating 127 algorithmic combinations was constructed, and SHAP analysis was applied to identify hub genes. Further analyses included functional enrichment, immune infiltration, miRNA regulatory network analysis, and SMR analysis. The expression patterns were validated using single-cell transcriptome data. Drug repositioning analysis and AI-assisted virtual screening were performed to prioritize compounds with favorable drug-like properties. The predicted binding modes of candidate compounds with CHEK1 were assessed by molecular docking. Results: Thirty core genes were obtained through differential expression, WGCNA, and PPI screening. Integrated ML (127 algorithms) determined the optimal model (AUC = 0.919), and SHAP identified nine feature genes, among which CHEK1 and KIF23 showed preliminary diagnostic potential across four external cohorts (AUC: 0.625–0.938). Functional enrichment indicated that both are enriched in the cell cycle and p53 pathways, closely associated with BRCA1/ATR; immune infiltration revealed significant correlations with macrophages and CD8⁺ T cells, with hsa-miR-15a-5p and hsa-miR-607 being common upstream regulatory miRNAs. SMR analysis supported a causal relationship between CHEK1 expression and BC genetic susceptibility (p_SMR < 0.05, p_HEIDI > 0.05); single-cell analysis confirms its heterogeneous expression. AI-assisted virtual screening identified 25 A-grade computational candidate compounds from 171 candidates. Molecular docking suggested that Olaparib and LY294002 can form favorable interactions with the CHEK1 active pocket. Conclusions: The study identified CHEK1 as a key diagnostic gene for BC through 127 ML algorithms and SMR causal inference. By combining AI-assisted virtual screening and molecular docking, computational candidate compounds targeting CHEK1 were prioritized. These findings represent hypothesis-generating in silico predictions and require experimental validation before any therapeutic conclusions can be drawn.

Introduction: Hepatocellular carcinoma (HCC) is one of the deadliest cancers. Traditional Chinese medicine has been used as an adjuvant therapy for HCC. Buqi Jianzhong Decoction (BJD), a classic liver-soothing formula with an undefined mechanism, was studied via multi-databases and Artificial Intelligence (AI) to complete key drug development steps. Methods: First, potential active ingredients and target genes were screened using the TCMSP database, and gene expression data from the GEO database were combined with AI algorithms to construct a disease-related gene network. Then, through AI-assisted screening with the Chembl database, candidate compounds highly correlated with core targets were identified. Next, molecular docking and molecular dynamics simulation techniques were used to assess the binding performance. Finally, experiments were conducted to validate the efficacy and safety of the candidate complexes. Results: The study found 17 potential core targets through protein-protein interaction network and GEO dataset analysis. Finally, the core targets were obtained based on a multi-algorithm approach. KEGG analysis indicated that BJD may inhibit HCC by targeting pathways. The AIbased ChEMBL database screening based on core targets identified 82 highly active compounds, and the top 20 compounds of pIC50 were selected for molecular docking with the core targets. Finally, it was verified that BJD could inhibit the proliferation, cloning, and migration of hepatocellular carcinoma cells, while reducing the p-PI3K, p-AKT, and β-catenin expression Discussion: BJD exerts therapeutic effects on HCC through multi-components, multi-targets, and multi-pathways, providing a theoretical basis for experimental studies. Conclusion: The results aided the modernization of TCM and the development of multitargeted cancer drugs

Background: Alzheimer’s disease (AD) is driven by complex molecular and immune dysregulation, yet reliable diagnostic biomarkers and druggable targets remain limited. This study aimed to identify key AD-associated regulatory genes, characterize their immune and spatial expression features, and prioritize small-molecule compounds with therapeutic potential. Methods: Multiple AD-related transcriptomic datasets—including bulk RNA-seq, microarray, and spatial transcriptomic profiles—were retrieved from GEO and systematically partitioned into discovery (GSE5281, GSE66333), validation (GSE110226, GSE28146, GSE29378), independent testing (GSE29378), and spatial validation cohorts (GSE147047). Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to construct co-expression networks and define AD-associated gene modules. Protein–protein interaction (PPI) analysis and multiple network centrality measures were then applied to prioritize candidate key genes. Twelve machine-learning algorithms were combined into 127 classification models, and SHAP-based interpretability analysis was used to quantify feature contributions and identify diagnostic genes. Single-cell and spatial transcriptomic data were further used to validate the cell type specificity and spatial localization of the hub genes. Drug–gene enrichment analysis (DSigDB), compound retrieval (PubChem), ADMET and drug-likeness profiling, and molecular blind docking were integrated to screen and evaluate potential lead compounds. Results: We identified 2,534 differentially expressed genes (DEGs) between AD and control samples, and their intersection with WGCNA-derived modules yielded 848 candidate genes. PPI-based network analysis prioritized 15 key genes, on which 127 machine-learning models were constructed; the random forest model achieved the best overall performance with an average AUC of 0.957. SHAP analysis identified 11 key diagnostic genes, among which IGF1R and SPP1 emerged as stable hub genes with AUCs greater than 0.70 across multiple external cohorts. Immune infiltration, single-cell, and spatial transcriptomic analyses demonstrated distinct immune associations and cell type– and region-specific expression patterns of these hub genes. Drug–gene enrichment identified 176 drug signatures and 445 related compounds, of which 37 grade-A molecules remained after ADMET and drug-likeness filtering. Molecular docking revealed four top-ranked compounds with binding energies better than −9.0 kcal/mol, including one ligand with a minimum binding energy of −10.5 kcal/mol and extensive non-covalent interactions with the target protein. Conclusion: A systematic methodological framework from gene discovery and diagnostic modeling to lead drug screening was developed in this study. IGF1R and SPP1 were identified as stable and biologically interpretable AD hub genes, which can be used as potential diagnostic markers, and various high-affinity small molecule compounds based on the hub genes provide new drug candidates for targeted AD therap.