The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for abundant computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. To address this, we propose a new computational workflow that combines knowledge on endogenous metabolism from a genome scale metabolic network (GSMN) and in vitro transcriptomics data with the aim of better identifying the metabolic MoA (mMoA) of chemicals. Our workflow proceeds in three main steps. The first step consists of building cell condition-specific models representing the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, based on these enumerations, two conditions can be compared by extracting differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes (PHH) transcriptomic data in Open TG-GATEs. Then, we applied this new workflow to model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). Despite large disparities in transcriptomic effects for these two chemicals, i.e. , two differentially expressed genes (DEGs) for amiodarone vs 5709 DEGs for valproic acid, our results well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. Author summary There is an urgent need for development and validation of new approach methodologies (NAMs) to avoid animal testing in safety evaluation. Among these NAMs, the exploration of big data and the use of transcriptomics data generated from in vitro systems are key. Omics data reflect how cellular biological processes are globally impacted by chemical exposure, but deciphering underlying modulations remains a challenge. In particular, interactions between biological processes are not taken into account, which does not allow for the analysis of MoAs spanning several processes. To this end, we propose an original workflow able to construct condition-specific metabolic networks from gene expression data to extract functional information about how endogenous metabolism is disrupted and visualize this mechanistic information thanks to a graph-based network analysis procedure. We highlight these new computational workflow capabilities by predicting and analyzing the metabolic impact of two known hepatotoxic compounds: amiodarone and valproic acid.