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Elucidating the specific enzymes responsible for metabolite formation is a cornerstone of drug discovery; it is essential for mapping metabolic pathways, assessing drug-drug interactions, driving lead optimization to enhance metabolic properties, and mitigating toxicity risk. While liquid chromatography-mass spectrometry (LC-MS/MS) excels at structural Metabolite Identification (MetID), experimental reaction phenotyping remains a major resource-intensive bottleneck. To bridge this gap, we present RevEng, an innovative computational workflow integrated within MetaSite 7¹. Taking the parent drug and a target metabolite (for example, an experimentally detected LC-MS/MS metabolite) as inputs, RevEng structurally reverse-engineers the metabolic space to reconstruct all potential metabolic pathways, including complex multi-step biotransformations, and assigns a probability score to the enzymes involved in each step. This inverse pathway prediction relies entirely on structure-based 3D docking and intrinsic chemical reactivity, using dedicated enzyme-specific predictive models for 23 Phase I (CYP and non-CYP) and 20 Phase II major human drug-metabolizing enzymes^2,3. These models are interaction- and reaction-based and are therefore fully independent of training-set-derived substrate knowledge. Enzyme active sites are characterized using GRID Molecular Interaction Fields (MIFs)⁴, while the docking procedure is specifically designed to mimic the biological and chemical determinants of each enzymatic reaction by evaluating substrate–residue interactions within the catalytic site. The resulting interaction energy, combined with an assessment of the molecule’s intrinsic reactivity toward a specific biotransformation, enables prediction of the most likely site of metabolism, related metabolites structures and the likelihood of metabolism by the enzyme under consideration. The workflow was rigorously validated against proprietary in-house experimental datasets, and the predicted inverse pathways were benchmarked against established literature data for diverse chemical entities. The 3D-docking engine successfully unraveled complex metabolic fates, accurately backtracking both single-step biotransformations and sequential, multi-step cascades (such as Phase I functionalization followed by Phase II conjugation). Beyond pathway elucidation, the prediction of the enzymes responsible for the formation of observed metabolites can directly drive experimental design, enabling the detection of metabolites using targeted enzymatic assays rather than complex biological matrices, thus leading to a better characterization of the metabolic profile of a drug. Furthermore, these predictions provide invaluable guidance for targeted metabolite synthesis and for anticipating potential toxicity liabilities. In conclusion, RevEng, in combination with the other advanced tools integrated within MetaSite 7, successfully bridges analytical chemistry and mechanism-based 3D docking, offering a chemically accurate and structurally-driven solution for automated reaction phenotyping and intelligent experimental design in early drug discovery.

1. Isoherranen, N., & Zanger, U. M. (2025). Cytochrome P450 enzymes in drug metabolism and precision medicine: Current challenges and future directions. ACS Pharmacology & Translational Science, 8(2), 114-131.
2. Cruciani, G., Carosati, E., De Boeck, B., Ethirajulu, K., Mackie, C., Howe, T., & Vianello, R. (2005). MetaSite: Understanding Metabolism in Human Cytochromes from the Perspective of the Chemist. Journal of Medicinal Chemistry, 48(22), 6970-6979.
3. Di, L., & Obach, R. S. (2024). The evolving landscape of non-CYP enzymes in drug metabolism: Current status and clinical relevance. Drug Metabolism Reviews, 56(2), 145-168.
4. Artese, A., Cross, S., Costa, G., Distinto, S., Parrotta, L., Alcaro, S., Ortuso, F., & Cruciani, G. (2013). Molecular interaction fields in drug discovery: recent advances and future perspectives. WIREs Computational Molecular Science, 3(6), 594-613.

Many drugs are metabolized in the human body to form metabolites for elimination. This metabolism is categorized into two classes of reactions. Phase I reactions are mediated by Cytochrome P450 enzymes, in addition to approximately thirty other enzymes. An additional twenty other enzymes are involved in Phase II reactions, which often result in conjugation products.

Most drugs undergo chemical transformations in the body, known as biotransformations, to generate metabolites that are more readily eliminated. These reactions are primarily mediated by metabolic enzymes, mainly in the liver, and are highly specific, with each enzyme favoring particular substrates. Identifying the enzymes responsible for metabolite formation is essential for elucidating metabolic pathways, predicting metabolic behavior, and assessing potential toxicity risks. Metabolite Identification (MetID) studies, typically conducted in vitro or in vivo, rely heavily on LC-MS/MS for detecting and structurally characterizing metabolites. However, most discovery studies provide limited insight into the enzymes involved, and experimental approaches for reaction phenotyping, such as recombinant enzyme incubations or chemical inhibition studies, are often time- and resource-intensive, limiting comprehensive pathway characterization.

To address this gap, we developed a workflow that integrates LC-MS/MS MetID data from in vitro incubations with computational predictions to identify enzymatic pathways responsible for metabolite formation, including both Phase I and Phase II reactions. The computational approach simulates interactions between xenobiotic compounds and human metabolic enzymes using their 3D structures, evaluating the exposure of reactive atoms to catalytic residues. Multiple docking poses are generated and scored based on energy contributions, and the best pose is normalized to provide a probability ranking.

MetID data were processed using MassMetaSite on the ONIRO server with LC-MS/MS datasets from Sciex and Thermo instruments, and predictions were performed using MetaSite 7 within Oniro. The model was applied to three compounds and validated against experimental CYP phenotyping data from the literature.

For dextromethorphan, Phase I metabolism studies using recombinant enzymes and human liver microsomes identified six metabolites formed through N- and O-dealkylation and hydroxylation after 30 minutes. Experimentally, CYP2D6 mediated O-dealkylation to dextrorphan, while CYP3A4 catalyzed N-dealkylation to 3-methoxymorphinan. The model accurately predicted both pathways with high probability, consistent with the experimental results.¹

Phase II metabolism of dextromethorphan was further evaluated in hepatocytes, and revealed a metabolite formed via O-dealkylation followed by glucuronidation after 140 minutes; the model correctly suggested CYP2D6 for the initial step and UGT enzymes for conjugation, in agreement with literature reports.²

Two additional compounds were also assessed for Phase I metabolism: thioridazine metabolism in human liver microsomes produced metabolites via S-oxidation and N-demethylation, with the model identifying CYP3A4 for 5-sulfoxide formation and suggested CYP1A2 as one enzyme involved in N-demethylation. The model also predicted CYP2D6 involvement in the formation of mesoridazine and its further conversion to sulphoridazine, consistent with published data.³ ⁴

For ethoxyresorufin, incubated with recombinant enzymes for 30 minutes, O-dealkylation to resorufin was observed in CYP1A2 incubations, and the model predicted involvement of both CYP1A2 and CYP1A1.⁵

Together, these results demonstrate the capability of the in silico functionality integrated within a MetID platform to predict Phase I and Phase II enzymatic pathways for LC-MS/MS identified metabolites.