This study aimed to assess the predictive value of baseline data in identifying patients at risk of developing liver chemistry signals during clinical trials. Using data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes based on baseline information, including demographics, medical history, concomitant medications, and laboratory results. The models achieved an average validation accuracy of around 80% in predicting liver signals. Baseline levels of individual liver chemistry tests were crucial for predicting their own elevations during the trials. High baseline bilirubin levels were common and associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT) levels showed some predictive value but did not significantly improve predictability beyond established liver chemistry tests. The study concluded that baseline data can be used to predict which patients are at higher risk of developing liver chemistry signals, allowing for proactive and targeted risk management. This approach could also help evaluate the performance of new biomarkers compared to established ones.This study aimed to assess the predictive value of baseline data in identifying patients at risk of developing liver chemistry signals during clinical trials. Using data from 24 late-stage clinical trials, classification models were developed to predict liver chemistry outcomes based on baseline information, including demographics, medical history, concomitant medications, and laboratory results. The models achieved an average validation accuracy of around 80% in predicting liver signals. Baseline levels of individual liver chemistry tests were crucial for predicting their own elevations during the trials. High baseline bilirubin levels were common and associated with a high risk of developing biochemical Hy’s law cases. Baseline γ-glutamyltransferase (GGT) levels showed some predictive value but did not significantly improve predictability beyond established liver chemistry tests. The study concluded that baseline data can be used to predict which patients are at higher risk of developing liver chemistry signals, allowing for proactive and targeted risk management. This approach could also help evaluate the performance of new biomarkers compared to established ones.