Artificial Intelligence Driven Virtual Screening and Molecular Docking Approaches Identified LIFR, BTG2, EPHX2, and PAK3 as Targets and BI-2536, AP-24534, and AZ-628 as Repurposed Drugs for PDAC

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal tumors worldwide, with limited effective treatments. Globally, the incidence of pancreatic cancer is expected to rise to 18.6 per 100,000 by 2050, with an average annual growth rate of 1.1%, implying that PDAC would represent a considerable public health burden. Identifying prognostic markers is critical for making therapy decisions and improving patient outcomes. In this study, the microarray gene expression data of PDAC were analyzed using artificial intelligence (AI) algorithms and molecular docking to identify the differentially expressed genes (DEGs) and drug repurposing. The GSE183795 dataset used in this study was obtained from the National Centre for Biotechnology Information. Further, the data were analyzed using GEO2R tools, and genes were selected based on logFC values>2. Then, these genes were ranked using AI algorithms such as support vector machine (SVM), logistic regression, random forest, extreme gradient boosting (XGB), and one-dimensional convolutional neural network to identify the DEGs. The performance of the models was evaluated using stratified 10-fold cross-validation and different classification metrics. A drug library was prepared using DepMap corresponding to the identified DEGs, and subsequently, molecular docking and pharmacokinetics analysis were performed. The result of the logFC>2 listed 107 upregulated genes in PDAC. It was observed that SVM and XGB show the average 10-fold accuracy, sensitivity, specificity, precision, and F-score of 79.25%, 78.37%, 78.37%, 79.33% and 78.35% respectively. Our results revealed that LIFR, BTG2, EPHX2, and PAK3 are within the top three and commonly ranked by AI models. Further, we identified three drugs, such as BI-2536, Ponatinib (AP-24534), and AZ-628, which show the best efficacy based on the binding energies by molecular docking analysis. The pharmacokinetics study strengthened our results that the identified drugs can be used as a therapeutic for PDAC as they obey Lipinski's rule. In conclusion, identified genes can act as prognostic markers, and drugs could be used as potential therapeutics for PDAC.