Insulin resistance is a critical factor in developing metabolic disorders like type 2 diabetes, posing challenges for effective treatment. Identifying molecular targets to reverse or mitigate insulin resistance is a key focus in therapeutic research. Advances in genomics and bioinformatics have enabled researchers to explore differentially expressed genes (DEGs) as potential biomarkers and therapeutic targets. This study aims to identify potential therapeutic targets for overcoming insulin resistance based on the analysis of (DEGs). Gallic acid (GA) and its derivatives were then tested against these identified targets using in silico methods. DEGs were analyzed from two Gene Expression Omnibus (GEO) datasets: GSE13070 (human adipose tissue with insulin resistance and insulin sensitivity) and GSE24422 (TNF-induced and non-induced adipocyte cell culture). The identified DEGs were then compared to find common DEGs, which were subsequently analyzed to identify hub-genes. Cross-validation using neural network and principal component analysis (PCA) on gene expression values revealed that the identified hub-genes, including IRS1, PCK1, GYS1, PTRPF, ACACB, and PIK3R2, can serve as biomarkers for insulin resistance (area under the curve, AUC 0.956 and sensitivity 1.00). The search for upstream regulatory proteins (URPs) of the hub-genes in the Comparative Toxicogenomics Database (CTD) indicated that the activities of TNF, PPARA, and AHR could influence the expression of several hub-genes, namely IRS1, PCK1, and ACACB. The activity prediction analysis, which was based on SkelSpheres molecular descriptors and confirmed by molecular docking, suggests that caffeoyl gallic acid may be a candidate compound for overcoming insulin resistance by inhibiting TNFA and activating PPARA. © 2024 Suryandari et al. and © 2024 the authors.