Site Of Metabolism Research Articles

Development of a Novel CYP3A4 Classifier Model via Site of Metabolism (SOM)-based Molecular Docking, Multivariate Analysis and Molecular Dynamics of Known Substrates and Inhibitors

CYP3A4 is a major hepatic enzyme essential for metabolizing diverse chemical entities. The development of CYP3A4 substrate and inhibitor classifier models is a valuable research strategy to prevent toxicokinetics. Currently, no molecular docking model is available to classify the substrates and inhibitors for a wide range of chemical entities. This study generated a CYP3A4 substrate-inhibitor classifier model using multivariate analysis of selected variables from site of metabolism (SOM)-based docking results and molecular descriptors. CYP3A4 SOM-based molecular docking experiment was performed on the substrates and inhibitors from the DrugBank database. The relevant descriptors were selected using the area under the receiving operating curve (AUROC) and boxplot analysis. The selected variables were used to generate a CYP3A4 substrate-inhibitor classifier model using multivariate analysis. Two complexes were selected for molecular dynamic simulations. A total of 326 substrates and 154 inhibitors were successfully docked. The SOM-based docking model predicted 78.5% SOM correctly of the substrates. The CDOCKER energy, improper dihedral energy, potential energy, initial RMS (root mean square) gradient, and RMS gradient demonstrated acceptable AUROC, sensitivity, specificity, and Youden’s index results. The selected variables were subjected to multivariate analysis, and the PLS-DA analysis classification model showed promising performance in predicting chemical entities’ behavior as substrates or inhibitors. This model classified the external samples correctly with over 70.0% prediction accuracy. Molecular dynamic simulations showed that the selected complexes obtained from the docking model produced stable complexes. The classifier model is applicable as a research tool for the early stages of drug discovery.

Semi-empirical and machine learning-based prediction of site of metabolisms mediated by aldehyde oxidase

Prediction of drug clearance by aldehyde oxidase (AO) catalyzed metabolism has been of great interest in drug discovery. We report a fast, accurate, and fully-automated tool that combines semi-empirical quantum mechanical (SQM) calculations and machine-learning (ML) technique to predict AO-mediated site of metabolism (SOM). We infer that calculated probability scores together with those based on approximate C(sp2)−H bond dissociation energies accomplish accurate and immediate prediction of the number and rank ordering of SOMs. Tests on a series of 150 AO substrates demonstrate that our computational strategy can be applicable to high-throughput screening of metabolically stable compounds.

Prediction of Sites of Metabolism of CYP3A4 Substrates Utilizing Docking-Derived Geometric Features.

Cytochrome P450 3A4 (CYP3A4) is one of the major drug-metabolizing enzymes in the human body and is responsible for the metabolism of ∼50% of clinically used drugs. Therefore, the identification of the compound's sites of metabolism (SOMs) mediated by CYP3A4 is of utmost importance in the early stage of drug discovery and development. Herein, docking-based approaches incorporating geometric features were used for SOMs prediction of CYP3A4 substrates. The cross-docking poses of a relatively large data set containing 474 substrates were analyzed in depth, and a widely observed geometric pattern called the close proximity of SOMs was derived from the poses. On the basis of the close proximity, several structure-based models have been constructed, which demonstrated better performance than those structure-based models using the criterion of Fe-SOM distance. For further improving the prediction performance, the structure-based models were also combined with the well-known ligand-based model SMARTCyp. One combined model exhibited good performance on the SOMs prediction of an external substrate set containing kinase inhibitors, PROTACs, approved drugs, and some lead compounds.

In Silico Prediction of Metabolic Reaction Catalyzed by Human Aldehyde Oxidase.

Aldehyde oxidase (AOX) plays an important role in drug metabolism. Human AOX (hAOX) is widely distributed in the body, and there are some differences between species. Currently, animal models cannot accurately predict the metabolism of hAOX. Therefore, more and more in silico models have been constructed for the prediction of the hAOX metabolism. These models are based on molecular docking and quantum chemistry theory, which are time-consuming and difficult to automate. Therefore, in this study, we compared traditional machine learning methods, graph convolutional neural network methods, and sequence-based methods with limited data, and proposed a ligand-based model for the metabolism prediction catalyzed by hAOX. Compared with the published models, our model achieved better performance (ACC = 0.91, F1 = 0.77). What's more, we built a web server to predict the sites of metabolism (SOMs) for hAOX. In summary, this study provides a convenient and automatable model and builds a web server named Meta-hAOX for accelerating the drug design and optimization stage.

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.

While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes. This article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways. A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM. Supplementary data are available at Bioinformatics online.

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning.

Unexpected metabolism in modification and conjugation phases can lead to the failure of many late-stage drug candidates or even withdrawal of approved drugs. Thus, it is critical to predict the sites of metabolism (SoM) for enzymes, which interact with drug-like molecules, in the early stages of the research. This study presents methods for predicting the isoform-specific metabolism for human AOs, FMOs, and UGTs and general CYP metabolism for preclinical species. The models use semi-empirical quantum mechanical simulations, validated using experimentally obtained data and DFT calculations, to estimate the reactivity of each SoM in the context of the whole molecule. Ligand-based models, trained and tested using high-quality regioselectivity data, combine the reactivity of the potential SoM with the orientation and steric effects of the binding pockets of the different enzyme isoforms. The resulting models achieve κ values of up to 0.94 and AUC of up to 0.92.

In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network

UDP-glucuronosyltransferases (UGTs) have gained increasing attention as they play important roles in the phase II metabolism of drugs. Due to the time-consuming process and high cost of experimental approaches to identify the metabolic fate of UGT enzymes, in silico methods have been developed to predict the UGT-mediated metabolism of drug-like molecules. We developed consensus models with the combination of machine learning (ML) and graph neural network (GNN) methods to predict if a drug-like molecule is a potential UGT substrate, and then we applied the Weisfeiler-Lehman Network (WLN) model to identify the sites of metabolism (SOMs) of UGT-catalyzed substrates. For the substrate model, the accuracy of the single substrate prediction model on the test set could reach to 0.835. Compared with the single estimators, the consensus models are more stable and have better generalization ability, and the accuracy on the test set reached to 0.851. For the SOM model, the top-1 accuracy of the SOM model on the test set reached to 0.898, outperforming existing works. Thus, in this study, we proposed a computational framework, named Meta-UGT, which would provide a useful tool for the prediction and optimization of metabolic profiles and drug design.Graphical

Synthesis, in silico prediction of sites of metabolism and in-vitro hepatotoxicity evaluationofnew seriesN’-substituted 3-(1,3,7-trimethyl-xanthin-8-ylthio)propanehydrazides

New series of 3-(1,3,7-trimethyl-xanthin-8-ylthio)propanehydrazide derivatives were designed and synthesized. The targed compounds were obtained in yields of 54 to 100% and their structures were elucidated by FTIR,1H NMR,13C NMR, MS and microanalyses. The tested compounds were subjected to in silico prediction of sites of metabolism (SOMs). The predictions show thatthe main metabolic changes will be primarily related to oxidation of the sulfur atom in the side chain, carried out under the action of CYP2C19, as well as O-demethylation of compounds containing methoxy groups.The N-demethylation of the xanthine fragment was determined to be regulated by CYP1A2, CYP2C9, CYP2D6 and CYP3A4. Theperformed in vitro studies confirmed for two of the tested compounds to be low hepatotoxic, due to the presented prooxidant effects at subcellular level (isolated rat liver microsomes). These results highlight these molecules as promising hydrazide-hydrazone structures for the design of compounds with low hepatotoxicity.

Site of Metabolism (SOM) Predictions for Centella asiatica and Orthosiphon stamineus Marker Compounds with CYP3A4 Using a Molecular Docking Approach

The human liver enzyme CYP3A4 plays an important role in the biotransformation of xenobiotics from herbs to harmless and excretable metabolites. However, CYP3A4 catalytic activity may also produce more toxic metabolites. Predicting sites of metabolism (SOMs) in the xenobiotics may prevent unwanted CYP3A4 products. The evidence to explain the comprehensive biotransformation of major compounds from Centella asiatica and Orthosiphon stamineus is inadequate. The aim of the present study was to determine the specific interaction between CYP3A4 ferric–oxidative active sites and potential SOMs for the major chemical compounds of C. asiatica and O. stamineus. Molecular docking on CYP3A4 ferric–oxo (bound to the protoporphirin group, HEME–Fe3+–O-) was carried out using the CDOCKER simulation program to predict the SOMs for compounds from C. asiatica (asiaticoside, madecassoside, asiatic acid and madecassic acid) and O. stamineus (sinensetin, eupatorin and 5-hydroxy-6,7,3',4'-tetramethoxyflavone (5TMF)). Binding energies, residue–ligand interactions, and SOMs were obtained from the analysis. The molecular docking results also revealed that sinensetin, eupatorin, and 5TMF bound strongly to the CYP3A4 active site with binding energies of -22.44, -34.29, and -25.84 kcal/mol, respectively. Generally, the residues Arg106, Ile301, Ther309, Glu374 and Ala307 were identified to have the highest number of interactions with eupatorine, 5TMF and sinensetin. The SOM prediction for eupatorine showed interactions between the oxygen of the HEME iron (ferric oxo) and the C-11 and O-4 atoms. The possible metabolism reactions were C-11-hydroxylation and O-4-demethylation. For 5TMF, the SOM prediction for interactions were at the C-13 and O-7 positions, which could undergo hydroxylation and O-demethylation reactions, respectively. The SOM of sinensetin was predicted at C-3’ and the possible metabolic reaction taking place would be hydroxylation. The present study concluded that the major compounds of O. stamineus have the potential to be metabolized by CYP3A4 through hydroxylation and O-demethylation reactions. These data may be useful for phytomedicine product development related to CYP3A4 biotransformation mechanisms.

Assessment of CYP2C9 Structural Models for Site of Metabolism Prediction.

Structure-based prediction of a compound's potential sites of metabolism (SOMs) mediated by cytochromes P450 (CYPs) is highly advantageous in the early stage of drug discovery. However, the accuracy of the SOMs prediction can be influenced by several factors. CYP2C9 is one of the major drug-metabolizing enzymes in humans and is responsible for the metabolism of ∼13 % of clinically used drugs. In this study, we systematically evaluated the effects of protein crystal structure models, scoring functions, heme forms, conserved active-site water molecules, and protein flexibility on SOMs prediction of CYP2C9 substrates. Our results demonstrated that, on average, ChemScore and GlideScore outperformed four other scoring functions: Vina, GoldScore, ChemPLP, and ASP. The performance of the crystal structure models with pentacoordinated heme was generally superior to that of the hexacoordinated iron-oxo heme (referred to as Compound I) models. Inclusion of the conserved active-site water molecule improved the prediction accuracy of GlideScore, but reduced the accuracy of ChemScore. In addition, the effect of the conserved water on SOMs prediction was found to be dependent on the receptor model and the substrate. We further found that one of snapshots from molecular dynamics simulations on the apo form can improve the prediction accuracy when compared to the crystal structural model.

Deciphering Reaction Determinants of Altered-Activity CYP2D6 Variants by Well-Tempered Metadynamics Simulation and QM/MM Calculations.

The xenobiotic metabolizing enzyme CYP2D6 is the P450 cytochrome family member with the highest rate of polymorphism. This causes changes in the enzyme activity and specificity, which can ultimately lead to adverse reactions during drug treatment. To avoid or lower CYP-related toxicity risks, prediction of the most likely positions within a molecule where a metabolic reaction might occur is paramount. In order to obtain accurate predictions, it is crucial to understand all phenomena within the active site of the enzyme that contribute to an efficient substrate recognition and the subsequent catalytic reaction together with their relative weight within the overall thermodynamic context. This study aims to define the weight of the driving forces upon the C-H bond activation within CYP2D6 wild-type and a clinically relevant allelic variant with increased activity (CYP2D6*53) featuring two amino acid mutations in close vicinity of the heme. First, we investigated the steric and electrostatic complementarity of the substrate bufuralol using well-tempered metadynamics simulations with the aim to obtain the free energy profiles for each site of metabolism (SoM) within the different active sites. Second, the stereoelectronic complementarity was determined for each SoM within the two different active-site environments. Relying on the well-tempered metadynamics simulation energy profiles of each SoM, we identified the binding mode that was closest to the preferred transition-state geometry for efficient C-H bond activation. The binding modes were then used as starting structures for the quantum mechanics/molecular mechanics calculations performed to quantify the corresponding activation barriers. Our results show the relevance of the steric component in orienting the SoM in an energetically accessible position toward the heme. However, the corresponding intrinsic reactivity and electronic complementarity within the active site must be accurately evaluated in order to obtain a meaningful reaction prediction, from which the predominant SoM can be determined. The F120I mutation lowered the activation barrier for the major site and one of the minor SoMs. However, it had an impact neither on the CYP2D6 enantioselectivity preference of the oxidation reaction nor on the stereoselectivity from the substrate point of view.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

New Proluciferin Substrates for Human CYP4 Family Enzymes.

We report the synthesis of seven new proluciferins for convenient activity determination of enzymes belonging to the cytochrome P450 (CYP) 4 family. Biotransformation of these probe substrates was monitored using each of the twelve human CYP4 family members, and eight were found to act at least on one of them. For all substrates, activity of CYP4Z1 was always highest, while that of CYP4F8 was always second highest. Site of metabolism (SOM) predictions involving SMARTCyp and docking experiments helped to rationalize the observed activity trends linked to substrate accessibility and reactivity. We further report the first homology model of CYP4F8 including suggested substrate recognition residues in a catalytically competent conformation accessed by replica exchange solute tempering (REST) simulations.

GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with reaction rule sets to predict and rank the structures of metabolites that could potentially be formed by phase 1 and/or phase 2 metabolism. GLORYx extends the approach from our previously developed tool GLORY, which predicted metabolite structures for cytochrome P450-mediated metabolism only. A robust approach to ranking the predicted metabolites is attained by using the SoM probabilities predicted by the FAME 3 machine learning models to score the predicted metabolites. On a manually curated test data set containing both phase 1 and phase 2 metabolites, GLORYx achieves a recall of 77% and an area under the receiver operating characteristic curve (AUC) of 0.79. Separate analysis of performance on a large amount of freely available phase 1 and phase 2 metabolite data indicates that achieving a meaningful ranking of predicted metabolites is more difficult for phase 2 than for phase 1 metabolites. GLORYx is freely available as a web server at https://nerdd.zbh.uni-hamburg.de/ and is also provided as a software package upon request. The data sets as well as all the reaction rules from this work are also made freely available.

Prediction of No Tropic Properties of Novel Drug Modafiendz using in-Silico Method

The development and approval of new drug is a tedious, expensive and highly time-consuming task. The demand of new drugs is increasing, and the development of natural drugs and traditional drugs are re-emerging as a new strategic task. The in-silico techniques have boosted the development of potential drug candidates. One important category of drugs is nootropes. It improves the cognitive function, memory, motivation and creativity in healthy individuals. The demand of the nootropic drugs has skyrocketed in past few decades. Modafiendz is a novel drug that is often used by the consumers as it is having similarity in structure and property with modafinil (Nootropic drug). But no major studies have been carried out on this molecule, so remains an investigatory molecule. There are several in-silico techniques that can be used to predict the likeness, metabolic activity and pharmacological property of a molecule. In the current study, realizing the importance of modafiendz, various properties of modafiendz have been predicted like metabolism site, ADME properties, metabolite prediction and DNA adduct formation tendencies.The properties of modafiendz were found to be similar to modafinil in various regards of drug likeability, bioavailability, blood brain permeability (BBB), GI absorption and site of metabolism (SOM). The study suggests modafiendz as a better nootropic drug candidate, when compared to modafinil.

PreMetabo: An in silico phase I and II drug metabolism prediction platform

This study aimed to develop a drug metabolism prediction platform using knowledge-based prediction models. Site of Metabolism (SOM) prediction models for four cytochrome P450 (CYP) subtypes were developed along with uridine 5′-diphosphoglucuronosyltransferase (UGT) and sulfotransferase (SULT) substrate classification models. The SOM substrate for a certain CYP was determined using the sum of the activation energy required for the reaction at the reaction site of the substrate and the binding energy of the substrate to the CYP enzyme. Activation energy was calculated using the EaMEAD model and binding energy was calculated by docking simulation. Phase II prediction models were developed to predict whether a molecule is the substrate of a certain phase II conjugate protein, i.e., UGT or SULT. Using SOM prediction models, the predictability of the major metabolite in the top-3 was obtained as 72.5–84.5% for four CYPs, respectively. For internal validation, the accuracy of the UGT and SULT substrate classification model was obtained as 93.94% and 80.68%, respectively. Additionally, for external validation, the accuracy of the UGT substrate classification model was obtained as 81% in the case of 11 FDA-approved drugs. PreMetabo is implemented in a web environment and is available at https://premetabo.bmdrc.kr/.

Novel Electrotopological Atomic Descriptors for the Prediction of Xenobiotic Cytochrome P450 Reactions.

Cytochrome P450 (CYP) is an enzyme family that plays a crucial role in metabolism, mainly metabolizing xenobiotics to produce non-toxic structures, however, some metabolized products can cause hepatotoxicity. Hence, predicting the structures of CYP products is an important task in designing non-hepatotoxic drugs. Here, we have developed novel atomic descriptors to predict the sites of metabolism (SoM) in CYP substrates. We proposed descriptors that describe topological and electrostatic characteristics of CYP substrates using Gasteiger charge. The proposed descriptors were applied to CYP3A4 data analysis as a case study. As a result of the descriptor selection, we obtained a gradient boosting decision tree-based SoM classification model that used 139 existing descriptors and the proposed 45 descriptors, and the model performed well in terms of the Matthews correlation coefficient. We also developed a structure converter to predict CYP products. This converter correctly generated 51 structural formulas of experimentally observed CYP3A4 products according to a manual evaluation.

Fast Methods for Prediction of Aldehyde Oxidase-Mediated Site-of-Metabolism

Aldehyde Oxidase (AO) is an enzyme involved in the metabolism of aldehydes and N-containing heterocyclic compounds. Many drug compounds contain heterocyclic moieties, and AO metabolism has lead to failure of several late-stage drug candidates. Therefore, it is important to take AO-mediated metabolism into account early in the drug discovery process, and thus, to have fast and reliable models to predict the site of metabolism (SOM). We have collected a dataset of 78 substrates of human AO with a total of 89 SOMs and 347 non-SOMs and determined atomic descriptors for each compound. The descriptors comprise NMR shielding and ESP charges from density functional theory (DFT), NMR chemical shift from ChemBioDraw, and Gasteiger charges from RDKit. Additionally, atomic accessibility was considered using 2D-SASA and relative span descriptors from SMARTCyp. Finally, stability of the product, the metabolite, was determined with DFT and also used as a descriptor. All descriptors have AUC larger than 0.75. In particular, descriptors related to the chemical shielding and chemical shift (AUC = 0.96) and ESP charges (AUC = 0.96) proved to be good descriptors. We recommend two simple methods to identify the SOM for a given molecule: 1) use ChemBioDraw to calculate the chemical shift or 2) calculate ESP charges or chemical shift using DFT. The first approach is fast but somewhat difficult to automate, while the second is more time-consuming, but can easily be automated. The two methods predict correctly 93% and 91%, respectively, of the 89 experimentally observed SOMs.

Computational Prediction of Site of Metabolism for UGT-Catalyzed Reactions.

The investigation of metabolically liable sites of xenobiotics mediated by UDP-glucuronosyltransferases (UGTs) is important for lead optimization in early drug discovery. However, it is time-consuming and costly to identify potentially susceptible sites experimentally. Hence, in silico approaches have been developed to predict the site of metabolism (SOM) of UGT-catalyzed substrates. In the present work, four major types of reactions catalyzed by UGTs were collected from the Handbook of Metabolic Pathways of Xenobiotics along with their corresponding glucuronidation products. These observed and nonobserved SOMs were identified and encoded by atom environment fingerprints. Four resampling methods were performed to treat data imbalance, while four feature selection methods were utilized to choose appropriate features. Three tree-form machine learning algorithms were conducted to build discriminating models, and optimal models were then obtained for the different types of reaction. The results indicated that all of the chosen best models showed area under the curve values ranging from 0.713 to 0.869 for two independent external validation sets. Our study could supply valuable information for optimization of pharmacokinetic profiles and contribute to metabolism prediction.

In silico prediction of Heterocyclic Aromatic Amines metabolism susceptible to form DNA adducts in humans

Heterocyclic Aromatic Amines (HAAs) are environmental and food contaminants that are classified as probable or possible carcinogens by the International Agency for Research on Cancer. Thirty different HAAs have been identified. However the metabolism of only three of them have been fully characterized in human hepatocytes: AαC (2-amino-9H-pyrido[2,3-b]indole), MeIQx (2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline) and PhIP (2-amino-1-methyl-6-phenyl-imidazo[4,5-b]pyridine). In this study, we use an integrative approach to accurately predict the biotransformation of 30 HAAs into DNA reactive and non DNA reactive compounds. We first build predicted metabolites networks by iterating a knowledge-based expert system of prediction of metabolic reactions based on fingerprint similarities. Next, we combine several methods for predicting Sites Of Metabolism (SOM) in order to reduce the metabolite reaction graphs and to predict the metabolites reactive with DNA. We validate the method by comparing the experimental versus predicted data for the known AαC, MeIQx and PhIP metabolism. 28 of the 30 experimentally determined metabolites are well predicted and 9 of the 10 metabolites known to form DNA adducts are predicted with a high probability to be reactive with DNA. Applying our approach to the 27 unknown HAAs, we generate maps for the metabolic biotransformation of each HAA, including new metabolites with a high-predicted DNA reactivity, which can be further explored through an user-friendly and interactive web interface.