The pipeline for in silico prediction of target genes and target metabolic pathways for small molecules, using chelidonic acid as an example

Modern pharmaceutical science is undergoing signi­ficant changes due to the active integration of computer techno­logies into the drug development process. Prior to the conventional stages of synthesis and experimental testing, researchers are increasingly turning to in silico methods, which enable the simulation of the structure and pharmacological effects of chemical compounds with a high degree of predictive accuracy. This approach significantly saves time and financial resources, optimizing subsequent experimental studies. In parallel, there is an active development of epigenome-targeted therapy, a new approach that allows for the modulation of gene expression involved in pathological processes without directly altering the primary DNA structure. Using chelidonic acid as a case study, we present a pipeline for computer-based assessment of small molecule effects on gene expression and metabolic pathways. The algorithm is based on the use of the DIGEP-Pred 2.0 web service, followed by multilevel bioinformatics analysis, inclu­ding: (1) gene over-representation analysis; (2) assessing the involvement of metabolic pathways; (3) functional clustering of pathways. The proposed approach can help solve problems in both fundamental and applied research on the molecular mechanisms of drug action.