Conducting late-stage clinical trials is a time-consuming and expensive process that spans several years and costs millions ( )). Therefore, developing, validating, and utilising predictive models earlier would be most advantageous, using preclinical and earlystage clinical trial data. Translational biomarkers can be forecasted by employing ma-chine learning techniques on preclinical datasets. Once the model and its accompanying biomarker have been validated using separate data sets from preclinical or clinical studies, they can be used to categorise patients, determine possible uses for medicine, and propose how the treatment works ([28])). For example, ML model used in COVID-19 to identify biomarkers ( )) (Figure ) Figure : The representation of ML application in biomarker identification