{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T03:03:24Z","timestamp":1789873404541,"version":"4.0.1"},"reference-count":251,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["963845"],"award-info":[{"award-number":["963845"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["965406"],"award-info":[{"award-number":["965406"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["964537"],"award-info":[{"award-number":["964537"]}]},{"name":"European Union\u2019s Horizon 2020 Research and Innovation Programme","award":["INNTA3\/2022\/13"],"award-info":[{"award-number":["INNTA3\/2022\/13"]}]},{"name":"Agencia Valenciana de la Investigaci\u00f3n","award":["963845"],"award-info":[{"award-number":["963845"]}]},{"name":"Agencia Valenciana de la Investigaci\u00f3n","award":["965406"],"award-info":[{"award-number":["965406"]}]},{"name":"Agencia Valenciana de la Investigaci\u00f3n","award":["964537"],"award-info":[{"award-number":["964537"]}]},{"name":"Agencia Valenciana de la Investigaci\u00f3n","award":["INNTA3\/2022\/13"],"award-info":[{"award-number":["INNTA3\/2022\/13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>The evolving landscape of chemical risk assessment is increasingly focused on developing tiered, mechanistically driven approaches that avoid the use of animal experiments. In this context, adverse outcome pathways have gained importance for evaluating various types of chemical-induced toxicity. Using hepatic steatosis as a case study, this review explores the use of diverse computational techniques, such as structure\u2013activity relationship models, quantitative structure\u2013activity relationship models, read-across methods, omics data analysis, and structure-based approaches to fill data gaps within adverse outcome pathway networks. Emphasizing the regulatory acceptance of each technique, we examine how these methodologies can be integrated to provide a comprehensive understanding of chemical toxicity. This review highlights the transformative impact of in silico techniques in toxicology, proposing guidelines for their application in evidence gathering for developing and filling data gaps in adverse outcome pathway networks. These guidelines can be applied to other cases, advancing the field of toxicological risk assessment.<\/jats:p>","DOI":"10.3390\/ijms252011154","type":"journal-article","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T08:56:32Z","timestamp":1729155392000},"page":"11154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6904-8833","authenticated-orcid":false,"given":"Rita","family":"Ortega-Vallbona","sequence":"first","affiliation":[{"name":"ProtoQSAR S.L., Calle Nicol\u00e1s Cop\u00e9rnico 6, Parque Tecnol\u00f3gico de Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7303-0743","authenticated-orcid":false,"given":"Martina","family":"Palomino-Sch\u00e4tzlein","sequence":"additional","affiliation":[{"name":"ProtoQSAR S.L., Calle Nicol\u00e1s Cop\u00e9rnico 6, Parque Tecnol\u00f3gico de Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1740-0874","authenticated-orcid":false,"given":"Laia","family":"Tolosa","sequence":"additional","affiliation":[{"name":"Unidad de Hepatolog\u00eda Experimental, Instituto de Investigaci\u00f3n Sanitaria La Fe (IIS La Fe), Av. Fernando Abril Martorell 106, 46026 Valencia, Spain"},{"name":"Biomedical Research Networking Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Instituto de Salud Carlos III, C\/Monforte de Lemos, 28029 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3976-5989","authenticated-orcid":false,"given":"Emilio","family":"Benfenati","sequence":"additional","affiliation":[{"name":"Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4209-6883","authenticated-orcid":false,"given":"Gerhard F.","family":"Ecker","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek Platz 2, 1090 Wien, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafael","family":"Gozalbes","sequence":"additional","affiliation":[{"name":"ProtoQSAR S.L., Calle Nicol\u00e1s Cop\u00e9rnico 6, Parque Tecnol\u00f3gico de Valencia, 46980 Paterna, Spain"},{"name":"MolDrug AI Systems S.L., Olimpia Arozena Torres 45, 46108 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8929-5364","authenticated-orcid":false,"given":"Eva","family":"Serrano-Candelas","sequence":"additional","affiliation":[{"name":"ProtoQSAR S.L., Calle Nicol\u00e1s Cop\u00e9rnico 6, Parque Tecnol\u00f3gico de Valencia, 46980 Paterna, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/toxres\/tfac087","article-title":"Risk assessment in the 21st century: Where are we heading?","volume":"12","author":"Brescia","year":"2023","journal-title":"Toxicol. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114650","DOI":"10.1016\/j.envres.2022.114650","article-title":"Application of AOPs to assist regulatory assessment of chemical risks\u2014Case studies, needs and recommendations","volume":"217","author":"Bajard","year":"2023","journal-title":"Environ. Res."},{"key":"ref_3","unstructured":"ECHA (2016, January 19\u201320). New Approach Methodologies in Regulatory Science. Proceedings of the Scientific Workshop, Helsinki, Finland."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1007\/s00204-023-03666-2","article-title":"Artificial intelligence (AI)\u2014it\u2019s the end of the tox as we know it (and I feel fine)","volume":"98","author":"Kleinstreuer","year":"2024","journal-title":"Arch. Toxicol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00204-019-02613-4","article-title":"Toxicity testing in the 21st century: Progress in the past decade and future perspectives","volume":"94","author":"Krewski","year":"2020","journal-title":"Arch. Toxicol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1111\/risa.13423","article-title":"Predicting the Probability that a Chemical Causes Steatosis Using Adverse Outcome Pathway Bayesian Networks (AOPBNs)","volume":"40","author":"Burgoon","year":"2020","journal-title":"Risk Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comtox.2018.08.003","article-title":"A mechanistic framework for integrating chemical structure and high-throughput screening results to improve toxicity predictions","volume":"8","author":"Nelms","year":"2018","journal-title":"Comput. Toxicol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.cotox.2019.02.006","article-title":"Omics-based input and output in the development and use of adverse outcome pathways","volume":"18","author":"Vinken","year":"2019","journal-title":"Curr. Opin. Toxicol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Portugal, J., Mansilla, S., and Pi\u00f1a, B. (2022). Perspectives on the Use of Toxicogenomics to Assess Environmental Risk. Front. Biosci., 27.","DOI":"10.31083\/j.fbl2710294"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.tox.2013.08.011","article-title":"The adverse outcome pathway concept: A pragmatic tool in toxicology","volume":"312","author":"Vinken","year":"2013","journal-title":"Toxicology"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Benfenati, E. (2022). Chapter 20: Adverse Outcome Pathways as Versatile Tools in Liver Toxicity Testing. In Silico Methods for Predicting Drug Toxicity, Springer Science+Business Media.","DOI":"10.1007\/978-1-0716-1960-5"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4661","DOI":"10.1021\/acs.est.6b06230","article-title":"Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology","volume":"51","author":"Conolly","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1002\/etc.4505","article-title":"Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment","volume":"38","author":"Perkins","year":"2019","journal-title":"Environ. Toxicol. Chem."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.1007\/s00204-017-2045-3","article-title":"Adverse outcome pathways: Opportunities, limitations and open questions","volume":"91","author":"Leist","year":"2017","journal-title":"Arch. Toxicol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1093\/toxsci\/kfu199","article-title":"Adverse outcome pathway (AOP) development I: Strategies and principles","volume":"142","author":"Villeneuve","year":"2014","journal-title":"Toxicol. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.2174\/138920012802850001","article-title":"Drug-induced Liver Steatosis and Phospholipidosis: Cell-Based Assays for Early Screening of Drug Candidates","volume":"13","author":"Donato","year":"2012","journal-title":"Curr. Drug. Metab."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pizzo, F., Lombardo, A., Manganaro, A., and Benfenati, E. (2016). A New Structure-Activity Relationship (SAR) Model for predicting drug-induced liver injury, based on statistical and expert-based structural alerts. Front. Pharmacol., 7.","DOI":"10.3389\/fphar.2016.00442"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.cbi.2009.07.019","article-title":"Cytometric analysis for drug-induced steatosis in HepG2 cells","volume":"181","author":"Donato","year":"2009","journal-title":"Chem. Biol. Interact."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1021\/acs.chemrestox.8b00112","article-title":"Adverse Outcome Pathway-Driven Analysis of Liver Steatosis in Vitro: A Case Study with Cyproconazole","volume":"31","author":"Luckert","year":"2018","journal-title":"Chem. Res. Toxicol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1021\/acs.chemrestox.5b00208","article-title":"Adverse Outcome Pathways and Drug-Induced Liver Injury Testing","volume":"28","author":"Vinken","year":"2015","journal-title":"Chem. Res. Toxicol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1021\/acs.chemrestox.0c00511","article-title":"Combining in Vivo Data with In Silico Predictions for Modeling Hepatic Steatosis by Using Stratified Bagging and Conformal Prediction","volume":"34","author":"Jain","year":"2021","journal-title":"Chem. Res. Toxicol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/toxsci\/kfw018","article-title":"Tipping the balance: Hepatotoxicity and the 4 apical key events of hepatic steatosis","volume":"150","author":"Angrish","year":"2016","journal-title":"Toxicol. Sciences"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1016\/j.jhep.2021.01.045","article-title":"Diagnosis and management of secondary causes of steatohepatitis","volume":"74","author":"Liebe","year":"2021","journal-title":"J. Hepatol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"153814","DOI":"10.1016\/j.tox.2024.153814","article-title":"A Quantitative Weight-of-Evidence Method for Confidence Assess-ment of Adverse Outcome Pathway Networks: A Case Study on Chemical-Induced Liver Steatosis","volume":"505","author":"Verhoeven","year":"2024","journal-title":"Toxicology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1021\/acs.jcim.8b00297","article-title":"QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis","volume":"58","author":"Gadaleta","year":"2018","journal-title":"J. Chem. Inf. Model."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105269","DOI":"10.1016\/j.tiv.2021.105269","article-title":"Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action","volume":"79","author":"Escher","year":"2022","journal-title":"Toxicol. In Vitro"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"152856","DOI":"10.1016\/j.tox.2021.152856","article-title":"Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity","volume":"459","author":"Arnesdotter","year":"2021","journal-title":"Toxicology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1038\/nrd4280","article-title":"Liver X receptors in lipid metabolism: Opportunities for drug discovery","volume":"13","author":"Hong","year":"2014","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1016\/j.addr.2010.07.006","article-title":"Functional crosstalk of CAR-LXR and ROR-LXR in drug metabolism and lipid metabolism","volume":"62","author":"Xiao","year":"2010","journal-title":"Adv. Drug Deliv. Rev."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1021\/acs.chemrestox.0c00006","article-title":"Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery","volume":"33","author":"Yang","year":"2020","journal-title":"Chem. Res. Toxicol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1021\/ci500611v","article-title":"Discovering structural alerts for mutagenicity using stable emerging molecular patterns","volume":"55","author":"Lepailleur","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1021\/acs.chemrestox.2c00368","article-title":"Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments","volume":"36","author":"Chakravarti","year":"2022","journal-title":"Chem. Res. Toxicol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Benfenati, E. (2016). QSAR Methods. In Silico Methods for Predicting Drug Toxicity, Springer Science+Business Media.","DOI":"10.1007\/978-1-4939-3609-0"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2945","DOI":"10.1021\/ci500314a","article-title":"Computational derivation of structural alerts from large toxicology data sets","volume":"54","author":"Ahlberg","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lepailleur, A., Poezevara, G., and Bureau, R. (2013). Automated detection of structural alerts (chemical fragments) in (eco)toxicology. Comput. Struct. Biotechnol. J., 5.","DOI":"10.5936\/csbj.201302013"},{"key":"ref_36","unstructured":"PubChem (2024, January 02). PubChem Substructure Fingerprint, Available online: https:\/\/ftp.ncbi.nlm.nih.gov\/pubchem\/specifications\/pubchem_fingerprints.pdf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1093\/bioinformatics\/btn479","article-title":"Chemical substructures that enrich for biological activity","volume":"24","author":"Klekota","year":"2008","journal-title":"Bioinformatics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13321-016-0125-7","article-title":"Bioalerts: A python library for the derivation of structural alerts from bioactivity and toxicity data sets","volume":"8","year":"2016","journal-title":"J. Cheminform"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7315","DOI":"10.1021\/ja00336a004","article-title":"Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules","volume":"106","author":"Klopman","year":"1984","journal-title":"J. Am. Chem. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1021\/ci300111r","article-title":"Optimizing Predictive Performance of CASE Ultra Expert System Models Using the Applicability Domains of Individual Toxicity Alerts","volume":"52","author":"Chakravarti","year":"2012","journal-title":"J. Chem. Inf. Model."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"52","DOI":"10.4103\/0976-500X.77119","article-title":"Prediction of activity spectra for substances","volume":"2","author":"Parasuraman","year":"2011","journal-title":"J. Pharmacol. Pharmacother."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1080\/1062936X.2013.773376","article-title":"Automatic knowledge extraction from chemical structures: The case of mutagenicity prediction","volume":"24","author":"Ferrari","year":"2013","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Nijssen, S., and Kok, J.N. (2004). A quickstart in frequent structure mining can make a difference. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM.","DOI":"10.1145\/1014052.1014134"},{"key":"ref_44","unstructured":"Borgelt, C., and Berthold, M.R. (2002, January 9\u201312). Mining molecular fragments: Finding relevant substructures of molecules. Proceedings of the 2002 IEEE International Conference on Data Mining, 2002. Proceedings, Maebashi, Japan."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1177\/074823378800400407","article-title":"An overview of structure-activity relationships as an alternative to testing in animals for carcinogenicity, mutagenicity, dermal and eye irritation, and acute oral toxicity","volume":"4","author":"Enslein","year":"1988","journal-title":"Toxicol. Ind. Health"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1177\/026119290503300508","article-title":"QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review","volume":"33","author":"Jaworska","year":"2005","journal-title":"Altern. Lab. Anim."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3494","DOI":"10.2174\/138161207782794257","article-title":"Predictive QSAR Modeling Workflow, Model Applicability Domains, and Virtual Screening","volume":"13","author":"Tropsha","year":"2007","journal-title":"Curr. Pharm. Des."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1517\/17425255.2010.486400","article-title":"Alternatives to the carcinogenicity bioassay: In silico methods, and the in vitro and in vivo mutagenicity assays","volume":"6","author":"Benigni","year":"2010","journal-title":"Expert Opin. Drug Metab. Toxico."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ferrari, T., Gini, G., Golbamaki Bakhtyari, N., and Benfenati, E. (2011, January 11\u201315). Mining toxicity structural alerts from SMILES: A new way to derive structure activity relationships. Proceedings of the 2011 IEEE Symposium on Computational Intelligence and Data Mining, Paris, France.","DOI":"10.1109\/CIDM.2011.5949444"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kortagere, S. (2013). Chapter 6 On Exploring Structure\u2014Activity Relationships. In Silico Models for Drug Discovery, Humana Press.","DOI":"10.1007\/978-1-62703-342-8"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1016\/j.scitotenv.2018.05.072","article-title":"QSARpy: A new flexible algorithm to generate QSAR models based on dissimilarities. The log Kow case study","volume":"637\u2013638","author":"Ferrari","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1021\/acs.chemrestox.5b00480","article-title":"Using Molecular Initiating Events to Develop a Structural Alert Based Screening Workflow for Nuclear Receptor Ligands Associated with Hepatic Steatosis","volume":"29","author":"Mellor","year":"2016","journal-title":"Chem. Res. Toxicol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/minf.201600142","article-title":"In Silico Identification of Proteins Associated with Drug-induced Liver Injury Based on the Prediction of Drug-target Interactions","volume":"36","author":"Ivanov","year":"2017","journal-title":"Mol. Inform."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e12294","DOI":"10.1016\/j.heliyon.2022.e12294","article-title":"Excessive gluconeogenesis causes the hepatic insulin resistance paradox and its sequelae","volume":"8","author":"Onyango","year":"2022","journal-title":"Heliyon"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1194\/jlr.M034249","article-title":"Disruption of uridine homeostasis links liver pyrimidine metabolism to lipid accumulation","volume":"54","author":"Le","year":"2013","journal-title":"J. Lipid Res."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Enoch, S., Mellor, C., and Nelms, M. (2018). Mitochondrial Structure and Function. Mitochondrial Dysfunction by Drug and Environmental Toxicants, Wiley.","DOI":"10.1002\/9781119329725.ch3"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/minf.202000005","article-title":"Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity","volume":"39","author":"Hemmerich","year":"2020","journal-title":"Mol. Inform."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1021\/acs.chemrestox.5b00275","article-title":"Development of an In Silico Profiler for Mitochondrial Toxicity","volume":"28","author":"Nelms","year":"2015","journal-title":"Chem. Res. Toxicol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1093\/toxsci\/kfs279","article-title":"The development of structure-activity relationships for mitochondrial dysfunction: Uncoupling of oxidative phosphorylation","volume":"131","author":"Naven","year":"2013","journal-title":"Toxicol. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"153764","DOI":"10.1016\/j.tox.2024.153764","article-title":"Uncovering the toxicity mechanisms of a series of carboxylic acids in liver cells through computational and experimental approaches","volume":"504","author":"Tolosa","year":"2024","journal-title":"Toxicology"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1021\/ci500667v","article-title":"New Publicly Available Chemical Query Language, CSRML, To Support Chemotype Representations for Application to Data Mining and Modeling","volume":"55","author":"Yang","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1021\/tx1000865","article-title":"Developing structure-activity relationships for the prediction of hepatotoxicity","volume":"23","author":"Greene","year":"2010","journal-title":"Chem. Res. Toxicol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Szalowska, E., Van Der Burg, B., Man, H.Y., Hendriksen, P.J.M., and Peijnenburg, A.A.C.M. (2014). Model steatogenic compounds (amiodarone, valproic acid, and tetracycline) alter lipid metabolism by different mechanisms in mouse liver slices. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0086795"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3525","DOI":"10.1039\/D0CS00098A","article-title":"QSAR without borders","volume":"49","author":"Muratov","year":"2020","journal-title":"Chem. Soc. Rev."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1007\/s11030-021-10277-5","article-title":"Computational strategies for the discovery of biological functions of health foods, nutraceuticals and cosmeceuticals: A review","volume":"25","author":"Carpio","year":"2021","journal-title":"Mol. Divers."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Roy, K. (2023). Chapter 10: Computational Modeling of DYRK1A Inhibitors as Potential Anti-Alzheimer Agents. Computational Modeling of Drugs Against Alzheimer\u2019s Disease, Springer Science+Business Media.","DOI":"10.1007\/978-1-0716-3311-3"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1039\/C8SC04175J","article-title":"Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations","volume":"10","author":"Winter","year":"2019","journal-title":"Chem. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best practices for QSAR model development, validation, and exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol. Inform."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Toropov, A.A., and Toropova, A.P. (2020). QSPR\/QSAR: State-of-Art, Weirdness, the Future. Molecules, 25.","DOI":"10.20944\/preprints202001.0325.v1"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/frai.2019.00017","article-title":"Descriptor Free QSAR Modeling Using Deep Learning With Long Short-Term Memory Neural Networks","volume":"2","author":"Chakravarti","year":"2019","journal-title":"Front. Artif. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1080\/17425255.2024.2334308","article-title":"Bioactivity descriptors for in vivo toxicity prediction: Now and the future","volume":"20","author":"Helmke","year":"2024","journal-title":"Expert Opin. Drug Metab. Toxicol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/s13321-022-00615-6","article-title":"KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study","volume":"14","author":"Manners","year":"2022","journal-title":"J. Cheminform."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"e1475","DOI":"10.1002\/wcms.1475","article-title":"In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways","volume":"10","author":"Hemmerich","year":"2020","journal-title":"Wiley Interdiscip. Rev. Comput. Mol. Sci."},{"key":"ref_74","unstructured":"North American Free Trade Agreement (NAFTA) Technical Working Group on Pesticides (TWG) (2012). (Quantitative) Structure Activity Relationship [(Q)SAR] Guidance Document, U.S. Environmental Protection Agency."},{"key":"ref_75","unstructured":"Worth, A., Lapenna, S., Piparo, E.L., and Serafimova, R. (2011). A Framework for Assessing In Silico Toxicity Predictions: Case Studies with Selected Pesticides, Publications Office of the European Union."},{"key":"ref_76","unstructured":"OECD (2023). (Q)SAR Assessment Framework: Guidance for the Regulatory Assessment of (Quantitative) Structure Activity Relationship Models and Predictions, OECD Publishing."},{"key":"ref_77","first-page":"1","article-title":"Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation","volume":"3","author":"Gozalbes","year":"2018","journal-title":"Int. J. Quant. Struct.-Prop. Relatsh."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.tox.2016.01.009","article-title":"The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPAR\u03b3 dysregulation","volume":"392","author":"Tsakovska","year":"2017","journal-title":"Toxicology"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.comtox.2019.01.004","article-title":"Ranking strategies to support toxicity prediction: A case study on potential LXR binders","volume":"10","author":"Palczewska","year":"2019","journal-title":"Comput. Toxicol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"127068","DOI":"10.1016\/j.chemosphere.2020.127068","article-title":"Predictive modeling of aryl hydrocarbon receptor (AhR) agonism","volume":"256","author":"Giner","year":"2020","journal-title":"Chemosphere"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/ijms21217853","article-title":"A toxicity prediction tool for potential agonist\/antagonist activities in molecular initiating events based on chemical structures","volume":"21","author":"Kurosaki","year":"2020","journal-title":"Int. J. Mol. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1093\/toxsci\/kfl103","article-title":"The ToxCast Program for Prioritizing Toxicity Testing of Environmental Chemicals","volume":"95","author":"Dix","year":"2007","journal-title":"Toxicol. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"163","DOI":"10.14573\/altex.1803011","article-title":"The US Federal Tox21 Program: A strategic and operational plan for continued leadership","volume":"35","author":"Thomas","year":"2018","journal-title":"ALTEX"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"126768","DOI":"10.1016\/j.chemosphere.2020.126768","article-title":"Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training","volume":"253","author":"Tang","year":"2020","journal-title":"Chemosphere"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1021\/acs.chemrestox.3c00086","article-title":"Predicting the Mitochondrial Toxicity of Small Molecules: Insights from Mechanistic Assays and Cell Painting Data","volume":"36","author":"Montanari","year":"2023","journal-title":"Chem. Res. Toxicol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"111494","DOI":"10.1016\/j.fct.2020.111494","article-title":"Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools","volume":"142","author":"Cotterill","year":"2020","journal-title":"Food Chem. Toxicol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Shin, H.K., Kang, M.-G., Park, D., Park, T., and Yoon, S. (2020). Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures. Front. Pharmacol., 11.","DOI":"10.3389\/fphar.2020.00067"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.drudis.2016.02.015","article-title":"DILIrank: The largest reference drug list ranked by the risk for developing drug-induced liver injury in humans","volume":"21","author":"Chen","year":"2016","journal-title":"Drug Discov. Today"},{"key":"ref_89","unstructured":"(2024, August 05). Elsevier: Pharmapendium. Available online: https:\/\/www.pharmapendium.com."},{"key":"ref_90","unstructured":"Fujitsu (2024, August 05). ADME Database. Available online: https:\/\/www.fujitsu.com\/global\/solutions\/business-technology\/tc\/sol\/admedatabase\/."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.comtox.2017.02.002","article-title":"Read-across of 90-day rat oral repeated-dose toxicity: A case study for selected n-alkanols","volume":"2","author":"Schultz","year":"2017","journal-title":"Comput. Toxicol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"140","DOI":"10.14573\/altex.2010062","article-title":"NAM-Supported Read-Across: From Case Studies to Regulatory Guidance in Safety Assessment","volume":"38","author":"Rovida","year":"2021","journal-title":"ALTEX"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Benfenati, E. (2022). Computational Modeling of Mixture Toxicity. In Silico Methods for Predicting Drug Toxicity, Humana.","DOI":"10.1007\/978-1-0716-1960-5"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1039\/C6EN00399K","article-title":"Addressing a bottle neck for regulation of nanomaterials: Quantitative read-across (Nano-QRA) algorithm for cases when only limited data is available","volume":"4","author":"Gajewicz","year":"2017","journal-title":"Environ. Sci. Nano"},{"key":"ref_95","unstructured":"ECHA (2017). Read-Across Assessment Framework (RAAF), European Chemicals Agency."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/978-1-0716-1787-8_22","article-title":"Artificial Intelligence in Drug Safety and Metabolism","volume":"2390","author":"Smith","year":"2022","journal-title":"Methods Mol. Biol."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.comtox.2017.05.003","article-title":"Navigating through the minefield of read-across tools: A review of in silico tools for grouping","volume":"3","author":"Patlewicz","year":"2017","journal-title":"Comput. Toxicol."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1021\/acs.chemrestox.3c00062","article-title":"High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence\/Machine Learning and Human Knowledge","volume":"36","author":"Yang","year":"2023","journal-title":"Chem. Res. Toxicol."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Benfenati, E. (2022). In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives. In Silico Methods for Predicting Drug Toxicity, Springer Science+Business Media.","DOI":"10.1007\/978-1-0716-1960-5"},{"key":"ref_100","unstructured":"ECHA (2024, January 16). Guidance on Information Requirements and Chemical Safety Assessment. Chapter R.6: QSARs and Grouping of Chemicals. Available online: https:\/\/echa.europa.eu\/guidance-documents\/guidance-on-information-requirements-and-chemical-safety-assessment."},{"key":"ref_101","unstructured":"OECD (2014). Guidance on Grouping of Chemicals, OECD Publishing."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Jeliazkova, N., Jaworska, J., and Worth, A.P. (2010). Open Source Tools for Read-Across and Category Formation. In Silico Toxicology, The Royal Society of Chemistry.","DOI":"10.1039\/9781849732093-00408"},{"key":"ref_103","unstructured":"Cefic-LRI (2023, December 12). Ambit\u2014Cheminformatics Data Management System. Available online: https:\/\/cefic-lri.org\/toolbox\/ambit\/."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1080\/1062936X.2015.1136680","article-title":"QSAR Toolbox\u2014Workflow and major functionalities","volume":"27","author":"Dimitrov","year":"2016","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1080\/1062936X.2014.976267","article-title":"ToxRead: A tool to assist in read across and its use to assess mutagenicity of chemicals","volume":"25","author":"Gini","year":"2014","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.yrtph.2008.05.012","article-title":"Toxmatch-A chemical classification and activity prediction tool based on similarity measures","volume":"52","author":"Poater","year":"2008","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_107","first-page":"462","article-title":"Generalized read-across (GENRA): A workflow implemented into the EPA comptox chemicals dashboard","volume":"36","author":"Helman","year":"2019","journal-title":"ALTEX"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"100272","DOI":"10.1016\/j.comtox.2023.100272","article-title":"The role of a molecular informatics platform to support next generation risk assessment","volume":"26","author":"Yang","year":"2023","journal-title":"Comput. Toxicol."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"142232","DOI":"10.1016\/j.chemosphere.2024.142232","article-title":"The VERA software: Implementation of the acute fish toxicity endpoint and its application to pharmaceutical compounds","volume":"358","author":"Colombo","year":"2024","journal-title":"Chemosphere"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.yrtph.2018.04.014","article-title":"In silico toxicology protocols","volume":"96","author":"Myatt","year":"2018","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.yrtph.2016.05.008","article-title":"Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information","volume":"79","author":"Shah","year":"2016","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"3643","DOI":"10.1007\/s00204-019-02591-7","article-title":"Towards grouping concepts based on new approach methodologies in chemical hazard assessment: The read-across approach of the EU-ToxRisk project","volume":"93","author":"Escher","year":"2019","journal-title":"Arch. Toxicol."},{"key":"ref_113","unstructured":"OECD (2020). Case Study on the Use of Integrated Approaches to Testing and Assessment for Prediction of a 90-Day Repeated Dose Toxicity Study (OECD 408) for 2-Ethylbutyric Acid Using a Read-Across Approach to Other Branched Carboxylic Acids, OECD Publishing."},{"key":"ref_114","first-page":"207","article-title":"Application of high-throughput transcriptomics for mechanism-based biological read-across of short-chain carboxylic acid analogues of valproic acid","volume":"39","author":"Vrijenhoek","year":"2022","journal-title":"ALTEX"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"104617","DOI":"10.1016\/j.yrtph.2020.104617","article-title":"Comparison of the potential mechanisms for hepatotoxicity of p-dialkoxy chlorobenzenes in rat primary hepatocytes for read-across","volume":"113","author":"Nakagawa","year":"2020","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1093\/toxsci\/kfx097","article-title":"The role of omics in the application of adverse outcome pathways for chemical risk assessment","volume":"158","author":"Brockmeier","year":"2017","journal-title":"Toxicol. Sci."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11831-021-09547-0","article-title":"Computational Techniques and Tools for Omics Data Analysis: State-of-the-Art, Challenges, and Future Directions","volume":"28","author":"Kaur","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"D1049","DOI":"10.1093\/nar\/gku1179","article-title":"Gene ontology consortium: Going forward","volume":"43","author":"Blake","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1093\/nar\/gkt1076","article-title":"Data, information, knowledge and principle: Back to metabolism in KEGG","volume":"42","author":"Kanehisa","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1093\/chromsci\/bmw167","article-title":"Proteomics: Technologies and Their Applications","volume":"55","author":"Aslam","year":"2017","journal-title":"J. Chromatogr. Sci."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"105143","DOI":"10.1016\/j.yrtph.2022.105143","article-title":"R-ODAF: Omics data analysis framework for regulatory application","volume":"131","author":"Verheijen","year":"2022","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.yrtph.2015.04.010","article-title":"Technical guide for applications of gene expression profiling in human health risk assessment of environmental chemicals","volume":"72","author":"Moffat","year":"2015","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1093\/toxres\/tfaa063","article-title":"A decade of toxicological trends: What the papers say","volume":"9","author":"Sikakana","year":"2020","journal-title":"Toxicol. Res. (Camb)"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"104621","DOI":"10.1016\/j.yrtph.2020.104621","article-title":"Towards the development of an omics data analysis framework","volume":"112","author":"Verheijen","year":"2020","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","article-title":"PubChem 2019 update: Improved access to chemical data","volume":"47","author":"Kim","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"D930","DOI":"10.1093\/nar\/gky1075","article-title":"ChEMBL: Towards direct deposition of bioassay data","volume":"47","author":"Mendez","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1097\/01.NAJ.0000443783.75162.79","article-title":"TOXNET: Information on toxicology and environmental health","volume":"114","author":"Fowler","year":"2014","journal-title":"Am. J. Nurs."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.2217\/14622416.7.7.1025","article-title":"Toxicogenomics in drug discovery and development: Mechanistic analysis of compound\/class-dependent effects using the DrugMatrix\u00ae database","volume":"7","author":"Ganter","year":"2006","journal-title":"Pharmacogenomics"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"D921","DOI":"10.1093\/nar\/gku955","article-title":"Open TG-GATEs: A large-scale toxicogenomics database","volume":"43","author":"Igarashi","year":"2015","journal-title":"Nucleic Acids Res."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1126\/science.1132939","article-title":"The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease","volume":"313","author":"Lamb","year":"2006","journal-title":"Science"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1016\/j.cell.2017.10.049","article-title":"A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles","volume":"171","author":"Subramanian","year":"2017","journal-title":"Cell"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"D948","DOI":"10.1093\/nar\/gky868","article-title":"The Comparative Toxicogenomics Database: Update 2019","volume":"47","author":"Davis","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1039\/C8MO00042E","article-title":"Developments in toxicogenomics: Understanding and predicting compound-induced toxicity from gene expression data","volume":"14","author":"Pruteanu","year":"2018","journal-title":"Mol. Omics"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Helikar, T., Kowal, B., McClenathan, S., Bruckner, M., Rowley, T., Madrahimov, A., Wicks, B., Shrestha, M., Limbu, K., and Rogers, J.A. (2012). The Cell Collective: Toward an open and collaborative approach to systems biology. BMC Syst. Biol., 6.","DOI":"10.1186\/1752-0509-6-96"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"D980","DOI":"10.1093\/nar\/gkab1059","article-title":"The European Genome-phenome Archive in 2021","volume":"50","author":"Freeberg","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"D82","DOI":"10.1093\/nar\/gkaa1028","article-title":"The European Nucleotide Archive in 2020","volume":"49","author":"Harrison","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"D1216","DOI":"10.1093\/nar\/gkab960","article-title":"The European Variation Archive: A FAIR resource of genomic variation for all species","volume":"50","author":"Cezard","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"D747","DOI":"10.1093\/nar\/gkl995","article-title":"ArrayExpress\u2014A public database of microarray experiments and gene expression profiles","volume":"35","author":"Parkinson","year":"2007","journal-title":"Nucleic Acids Res."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"D975","DOI":"10.1093\/nar\/gkt1211","article-title":"NCBI\u2019s Database of Genotypes and Phenotypes: dbGaP","volume":"42","author":"Tryka","year":"2014","journal-title":"Nucleic Acids Res."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"D129","DOI":"10.1093\/nar\/gkab1030","article-title":"Expression Atlas update: Gene and protein expression in multiple species","volume":"50","author":"Moreno","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/978-1-4939-3578-9_5","article-title":"The Gene Expression Omnibus database","volume":"1418","author":"Clough","year":"2016","journal-title":"Methods Mol. Biol."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: Archive for functional genomics data sets\u2014Update","volume":"41","author":"Barrett","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"D54","DOI":"10.1093\/nar\/gkr854","article-title":"The sequence read archive: Explosive growth of sequencing data","volume":"40","author":"Kodama","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"D1107","DOI":"10.1093\/nar\/gkw1080","article-title":"jPOSTrepo: An international standard data repository for proteomes","volume":"45","author":"Okuda","year":"2017","journal-title":"Nucleic Acids Res."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1038\/s41592-020-0955-0","article-title":"MassIVE.quant: A community resource of quantitative mass spectrometry\u2013based proteomics datasets","volume":"17","author":"Choi","year":"2020","journal-title":"Nat. Methods"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"100640","DOI":"10.1016\/j.mcpro.2023.100640","article-title":"PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts","volume":"22","author":"Huang","year":"2023","journal-title":"Mol. Cell. Proteom."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"D659","DOI":"10.1093\/nar\/gkj138","article-title":"PRIDE: A public repository of protein and peptide identifications for the proteomics community","volume":"34","author":"Jones","year":"2006","journal-title":"Nucleic Acids Res."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1038\/nbt.2839","article-title":"ProteomeXchange provides globally coordinated proteomics data submission and dissemination","volume":"32","author":"Deutsch","year":"2014","journal-title":"Nat. Biotechnol."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1038\/nature13319","article-title":"Mass-spectrometry-based draft of the human proteome","volume":"509","author":"Wilhelm","year":"2014","journal-title":"Nature"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"D622","DOI":"10.1093\/nar\/gkab1062","article-title":"HMDB 5.0: The Human Metabolome Database for 2022","volume":"50","author":"Wishart","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"14.13.1","DOI":"10.1002\/0471250953.bi1413s53","article-title":"MetaboLights: An Open-Access Database Repository for Metabolomics Data","volume":"53","author":"Kale","year":"2016","journal-title":"Curr. Protoc. Bioinform."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"D463","DOI":"10.1093\/nar\/gkv1042","article-title":"Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools","volume":"44","author":"Sud","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"ref_153","unstructured":"National Research Council (2007). Applications of Toxicogenomic Technologies to Predictive Toxicology and Risk Assessment, National Academies Press."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1289\/ehp.02110s6919","article-title":"Application of genomics to toxicology research","volume":"110","author":"Thomas","year":"2002","journal-title":"Environ. Health Perspect."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"154320","DOI":"10.1016\/j.metabol.2020.154320","article-title":"The role of omics in the pathophysiology, diagnosis and treatment of non-alcoholic fatty liver disease","volume":"111","author":"Perakakis","year":"2020","journal-title":"Metabolism"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s00204-017-2036-4","article-title":"Customised in vitro model to detect human metabolism-dependent idiosyncratic drug-induced liver injury","volume":"92","author":"Tolosa","year":"2018","journal-title":"Arch. Toxicol."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1053\/j.gastro.2013.12.032","article-title":"Pathogenesis of idiosyncratic drug-induced liver injury and clinical perspectives","volume":"146","author":"Fontana","year":"2014","journal-title":"Gastroenterology"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/toxsci\/kfab069","article-title":"Analytical and Omics-Based Advances in the Study of Drug-Induced Liver Injury","volume":"183","author":"Kralj","year":"2021","journal-title":"Toxicol. Sci."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.2217\/14622416.9.11.1695","article-title":"PXR: A xenobiotic receptor of diverse function implicated in pharmacogenetics","volume":"9","author":"Zhang","year":"2008","journal-title":"Pharmacogenomics"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1194\/jlr.R008896","article-title":"Thematic review series: Genetics of human lipid diseases\u2014Genetic determinants of hepatic steatosis in man","volume":"52","author":"Hooper","year":"2011","journal-title":"J. Lipid Res."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.jhep.2020.04.003","article-title":"Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically characterised cohort\u2606","volume":"73","author":"Anstee","year":"2020","journal-title":"J. Hepatol."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.cotox.2018.11.005","article-title":"Aryl hydrocarbon receptor (AhR) ligands as selective AhR modulators: Genomic studies","volume":"11","author":"Safe","year":"2018","journal-title":"Curr. Opin. Toxicol."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.cotox.2019.05.004","article-title":"Considerations for strategic use of high-throughput transcriptomics chemical screening data in regulatory decisions","volume":"15","author":"Harrill","year":"2019","journal-title":"Curr. Opin. Toxicol."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"S36","DOI":"10.1016\/j.yrtph.2017.11.001","article-title":"A generic Transcriptomics Reporting Framework (TRF) for \u2018omics data processing and analysis","volume":"91","author":"Gant","year":"2017","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1002\/etc.374","article-title":"Reverse engineering adverse outcome pathways","volume":"30","author":"Perkins","year":"2011","journal-title":"Environ. Toxicol. Chem."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1002\/hep.22907","article-title":"A novel pregnane X receptor and S14-mediated lipogenic pathway in human hepatocyte","volume":"49","author":"Moreau","year":"2009","journal-title":"Hepatology"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"114757","DOI":"10.1016\/j.taap.2019.114757","article-title":"Identification of differentially expressed genes and networks related to hepatic lipid dysfunction","volume":"382","author":"Abedini","year":"2019","journal-title":"Toxicol. Appl. Pharmacol."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fgene.2019.01007","article-title":"Mining Public Toxicogenomic Data Reveals Insights and Challenges in Delineating Liver Steatosis Adverse Outcome Pathways","volume":"10","author":"AbdulHameed","year":"2019","journal-title":"Front. Genet."},{"key":"ref_169","unstructured":"Heifetz, A. (2022). Machine Learning from Omics Data. Artificial Intelligence in Drug Design, Springer Science+Business Media, LLC, part of Springer Nature."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"219","DOI":"10.14573\/altex.1602071","article-title":"Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways Using Gene Expression Data","volume":"34","author":"Carbonell","year":"2017","journal-title":"ALTEX"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1021\/acs.jproteome.3c00009","article-title":"Integration of Metabolomics, Lipidomics, and Proteomics Reveals the Metabolic Characterization of Nonalcoholic Steatohepatitis","volume":"22","author":"Zhang","year":"2023","journal-title":"J. Proteome Res."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.cbpa.2008.07.024","article-title":"Mass spectrometry for proteomics","volume":"12","author":"Han","year":"2008","journal-title":"Curr. Opin. Chem. Biol."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"eaax8978","DOI":"10.1126\/sciadv.aax8978","article-title":"Beyond mass spectrometry, the next step in proteomics","volume":"6","author":"Timp","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_174","unstructured":"Gautier, J. (2011). Differential Proteomics Incorporating iTRAQ Labeling and Multi-dimensional Separations. Drug Safety Evaluation: Methods and Protocols, Springer Science+Business Media."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1002\/pmic.201400288","article-title":"Proteomics in mechanistic toxicology: History, concepts, achievements, caveats, and potential","volume":"15","author":"Rabilloud","year":"2015","journal-title":"Proteomics"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1002\/pmic.201400115","article-title":"Proteomic profile of carbonylated proteins in rat liver: Discovering possible mechanisms for tetracycline-induced steatosis","volume":"15","author":"Deng","year":"2015","journal-title":"Proteomics"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s00204-018-2360-3","article-title":"In vitro proteomic analysis of methapyrilene toxicity in rat hepatocytes reveals effects on intermediary metabolism","volume":"93","author":"Braeuning","year":"2019","journal-title":"Arch. Toxicol."},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, A.M., Carvalho, F., De Pinho, P.G., and Carvalho, M. (2021). Toxicometabolomics: Small molecules to answer big toxicological questions. Metabolites, 11.","DOI":"10.3390\/metabo11100692"},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1615\/CritRevBiomedEng.2013007736","article-title":"Metabolomic fingerprinting: Challenges and opportunities","volume":"41","author":"Kosmides","year":"2013","journal-title":"Crit. Rev. Biomed. Eng."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ddtec.2015.03.001","article-title":"Metabolomics in the pharmaceutical industry","volume":"13","author":"Reily","year":"2015","journal-title":"Drug Discov. Today Technol."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1002\/jat.2874","article-title":"Review: Toxicometabolomics","volume":"33","author":"Bouhifd","year":"2013","journal-title":"J. Appl. Toxicol."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2903\/j.efsa.2023.e211005","article-title":"Toxicometabolomics as a tool for next generation environmental risk assessment","volume":"21","author":"Bernhard","year":"2023","journal-title":"EFSA J."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.toxlet.2017.12.015","article-title":"Metabolomics profiling of steatosis progression in HepaRG\u00ae cells using sodium valproate","volume":"286","author":"Cuykx","year":"2018","journal-title":"Toxicol. Lett."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"41047","DOI":"10.1039\/D0RA06577C","article-title":"Early detection of metabolic changes in drug-induced steatosis using metabolomics approaches","volume":"10","author":"Yong","year":"2020","journal-title":"RSC Adv."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"2294","DOI":"10.1002\/elps.201500095","article-title":"LC-MS untargeted metabolomic analysis of drug-induced hepatotoxicity in HepG2 cells","volume":"36","author":"Castell","year":"2015","journal-title":"Electrophoresis"},{"key":"ref_186","first-page":"1","article-title":"A metabolomics cell-based approach for anticipating and investigating drug-induced liver injury","volume":"6","author":"Castell","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1007\/s00204-023-03474-8","article-title":"Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved","volume":"97","author":"Moro","year":"2023","journal-title":"Arch. Toxicol."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/bib\/bbab454","article-title":"A roadmap for multi-omics data integration using deep learning","volume":"23","author":"Kang","year":"2022","journal-title":"Brief. Bioinform."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"101254","DOI":"10.1016\/j.jksus.2020.101254","article-title":"A brief overview to systems biology in toxicology: The journey from in to vivo, in-vitro and \u2013omics","volume":"33","author":"Yahya","year":"2021","journal-title":"J. King Saud. Univ. Sci."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1177\/1177932219899051","article-title":"Multi-omics Data Integration, Interpretation, and Its Application","volume":"14","author":"Subramanian","year":"2020","journal-title":"Bioinform. Biol. Insights"},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Flores, J.E., Claborne, D.M., Weller, Z.D., Waters, K.M., and Bramer, L.M. (2013). Missing data in multi-omics integration: Recent advances through artificial intelligence. Front. Artif. Intel., 6.","DOI":"10.3389\/frai.2023.1098308"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csbj.2021.06.030","article-title":"Integration strategies of multi-omics data for machine learning analysis","volume":"19","author":"Picard","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1011224","article-title":"Ten quick tips for avoiding pitfalls in multi-omics data integration analyses","volume":"19","author":"Chicco","year":"2023","journal-title":"PLoS Comput. Biol."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s00204-020-02656-y","article-title":"Prospects and challenges of multi-omics data integration in toxicology","volume":"94","author":"Canzler","year":"2020","journal-title":"Arch. Toxicol."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1093\/toxsci\/65.1.135","article-title":"Genomics and proteomics analysis of acetaminophen toxicity in mouse liver","volume":"65","author":"Ruepp","year":"2002","journal-title":"Toxicol. Sci."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.tox.2017.11.013","article-title":"Integrative omics data analyses of repeated dose toxicity of valproic acid in vitro reveal new mechanisms of steatosis induction","volume":"393","author":"Claessen","year":"2018","journal-title":"Toxicology"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.toxlet.2018.02.026","article-title":"Integrated \u2018omics analysis reveals new drug-induced mitochondrial perturbations in human hepatocytes","volume":"289","author":"Wolters","year":"2018","journal-title":"Toxicol. Lett."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"102230","DOI":"10.1016\/j.clinre.2023.102230","article-title":"Applying proteomics in metabolic dysfunction-associated steatotic liver disease: From mechanism to biomarkers","volume":"47","author":"Zhang","year":"2023","journal-title":"Clin. Res. Hepatol. Gastroenterol."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/01926230490518244","article-title":"Toxicoproteomics: Proteomics applied to toxicology and pathology","volume":"32","author":"Wetmore","year":"2004","journal-title":"Toxicol. Pathol."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11306-016-1108-4","article-title":"The metabolome 18 years on: A concept comes of age","volume":"12","author":"Kell","year":"2016","journal-title":"Metabolomics"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/17425255.2017.1246535","article-title":"Utility of the adverse outcome pathway concept in drug development","volume":"13","author":"Hartung","year":"2017","journal-title":"Expert. Opin. Drug Metab. Toxicol."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1038\/nrd1549","article-title":"Docking and scoring in virtual screening for drug discovery: Methods and applications","volume":"3","author":"Kitchen","year":"2004","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"146","DOI":"10.2174\/157340911795677602","article-title":"Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery","volume":"7","author":"Meng","year":"2011","journal-title":"Curr. Comput.-Aided Drug Design. Curr. Comput. Aided Drug Des."},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Pinzi, L., and Rastelli, G. (2019). Molecular docking: Shifting paradigms in drug discovery. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20184331"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1111\/ijfs.14325","article-title":"Recent developments in molecular docking technology applied in food science: A review","volume":"55","author":"Tao","year":"2020","journal-title":"Int. J. Food Sci. Technol."},{"key":"ref_206","first-page":"105920","article-title":"How good are AlphaFold models for docking-based virtual screening?","volume":"26","author":"Scardino","year":"2022","journal-title":"ChemRxiv"},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1002\/anie.199423751","article-title":"The Key\u2013Lock Theory and the Induced Fit Theory","volume":"33","author":"Koshland","year":"1995","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1002\/minf.201400188","article-title":"Screening Chemicals for Receptor-Mediated Toxicological and Pharmacological Endpoints: Using Public Data to Build Screening Tools within a KNIME Workflow","volume":"34","author":"Steinmetz","year":"2015","journal-title":"Mol. Inform."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility","volume":"30","author":"Morris","year":"2009","journal-title":"J. Comput. Chem."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.csbj.2022.11.047","article-title":"CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein\u2013ligand docking","volume":"21","author":"Kwon","year":"2023","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1002\/jcc.23905","article-title":"DOCK 6: Impact of new features and current docking performance","volume":"36","author":"Allen","year":"2015","journal-title":"J. Comput. Chem."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1758-2946-1-15","article-title":"Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock","volume":"1","author":"Bikadi","year":"2009","journal-title":"J. Cheminform."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1002\/jcc.26050","article-title":"GalaxyDock3: Protein\u2013ligand docking that considers the full ligand conformational flexibility","volume":"40","author":"Yang","year":"2019","journal-title":"J. Comput. Chem."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1006\/jmbi.1996.0897","article-title":"Development and validation of a genetic algorithm for flexible docking","volume":"267","author":"Jones","year":"1997","journal-title":"J. Mol. Biol."},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"6177","DOI":"10.1021\/jm051256o","article-title":"Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein\u2212Ligand Complexes","volume":"49","author":"Friesner","year":"2006","journal-title":"J. Med. Chem."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1021\/ja026939x","article-title":"HADDOCK: A Protein\u2212Protein Docking Approach Based on Biochemical or Biophysical Information","volume":"125","author":"Dominguez","year":"2003","journal-title":"J. Am. Chem. Soc."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1021\/acs.jcim.8b00905","article-title":"V: MedusaDock 2.0: Efficient and Accurate Protein\u2013Ligand Docking With Constraints","volume":"59","author":"Wang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"1698","DOI":"10.1093\/bioinformatics\/btt262","article-title":"pyDockWEB: A web server for rigid-body protein\u2013protein docking using electrostatics and desolvation scoring","volume":"29","author":"Pons","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_220","doi-asserted-by":"crossref","first-page":"W233","DOI":"10.1093\/nar\/gkn216","article-title":"The RosettaDock server for local protein-protein docking","volume":"36","author":"Lyskov","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"W270","DOI":"10.1093\/nar\/gkr366","article-title":"SwissDock, a protein-small molecule docking web service based on EADock DSS","volume":"39","author":"Grosdidier","year":"2011","journal-title":"Nucleic Acids Res."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1002\/jcc.20291","article-title":"GROMACS: Fast, flexible, and free","volume":"26","author":"Lindahl","year":"2005","journal-title":"J. Comput. Chem."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"1668","DOI":"10.1002\/jcc.20290","article-title":"The Amber biomolecular simulation programs","volume":"26","author":"Case","year":"2005","journal-title":"J. Comput. Chem."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"1781","DOI":"10.1002\/jcc.20289","article-title":"Scalable molecular dynamics with NAMD","volume":"26","author":"Phillips","year":"2005","journal-title":"J. Comput. Chem."},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1002\/jcc.21287","article-title":"CHARMM: The biomolecular simulation program","volume":"30","author":"Brooks","year":"2009","journal-title":"J. Comput. Chem."},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Bowers, K.J., Sacerdoti, F.D., Salmon, J.K., Shan, Y., Shaw, D.E., Chow, E., Xu, H., Dror, R.O., Eastwood, M.P., and Gregersen, B.A. (2006;, January 11\u201317). Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. Proceedings of the 2006 ACM\/IEEE Conference on Supercomputing, Tampa, FL, USA.","DOI":"10.1109\/SC.2006.54"},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Hong, H. (2019). Applications of Molecular Dynamics Simulations in Computational Toxicology. Advances in Computational Toxicology, Springer.","DOI":"10.1007\/978-3-030-16443-0"},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.fct.2017.08.028","article-title":"Molecular dynamics simulations and applications in computational toxicology and nanotoxicology","volume":"112","author":"Selvaraj","year":"2018","journal-title":"Food Chem. Toxicol."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"117855","DOI":"10.1016\/j.envpol.2021.117855","article-title":"An adverse outcome pathway based in vitro characterization of novel flame retardants-induced hepatic steatosis","volume":"289","author":"Negi","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"7651","DOI":"10.3390\/ijms15057651","article-title":"Molecular modelling study of the PPAR\u03b3 receptor in relation to the mode of action\/adverse outcome pathway framework for liver steatosis","volume":"15","author":"Tsakovska","year":"2014","journal-title":"Int. J. Mol. Sci."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.yrtph.2017.02.008","article-title":"In silico design, chemical synthesis and toxicological evaluation of 1,3-thiazolidine-2,4-dione derivatives as PPAR\u03b3 agonists","volume":"86","year":"2017","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1177\/07482337231166490","article-title":"Postnatal exposure to Bisphenol S induces liver injury in mice: Possible implication of PPAR\u03b3 receptor","volume":"39","author":"Mornagui","year":"2023","journal-title":"Toxicol. Ind. Health"},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"153412","DOI":"10.1016\/j.tox.2022.153412","article-title":"Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking","volume":"485","author":"Moore","year":"2023","journal-title":"Toxicology"},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"100123","DOI":"10.1016\/j.comtox.2020.100123","article-title":"Identification of mitochondrial toxicants by combined in silico and in vitro studies\u2014A structure-based view on the adverse outcome pathway","volume":"14","author":"Troger","year":"2020","journal-title":"Comput. Toxicol."},{"key":"ref_235","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1021\/ci400649p","article-title":"Endocrine disruptome\u2014An open source prediction tool for assessing endocrine disruption potential through nuclear receptor binding","volume":"54","author":"Mavri","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.impact.2017.07.002","article-title":"Review of (Q)SAR models for regulatory assessment of nanomaterials risks","volume":"8","author":"Burello","year":"2017","journal-title":"NanoImpact"},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1080\/10629360701303727","article-title":"The specificity of the QSAR models for regulatory purposes: The example of the DEMETRA project\u2020","volume":"18","author":"Benfenati","year":"2007","journal-title":"SAR QSAR Environ. Res."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.toxrep.2018.08.017","article-title":"The use of structural alerts to avoid the toxicity of pharmaceuticals","volume":"5","author":"Limban","year":"2018","journal-title":"Toxicol. Rep."},{"key":"ref_239","unstructured":"OECD (2014). Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models, OECD Publishing."},{"key":"ref_240","unstructured":"OECD (2024, January 14). OECD Omics Reporting Framework (OORF): Guidance on Reporting Elements for the Regulatory Use of Omics Data from Laboratory-Based Toxicology Studies. Available online: https:\/\/doi.org\/10.1787\/6bb2e6ce-e."},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1002\/etc.4869","article-title":"Assessing the Ecological Risks of Per- and Polyfluoroalkyl Substances: Current State-of-the Science and a Proposed Path Forward","volume":"40","author":"Ankley","year":"2021","journal-title":"Environ. Toxicol. Chem."},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"111116","DOI":"10.1016\/j.fct.2020.111116","article-title":"Molecular modelling methods in food safety: Bisphenols as case study","volume":"137","author":"Cavaliere","year":"2020","journal-title":"Food Chem. Toxicol."},{"key":"ref_243","doi-asserted-by":"crossref","unstructured":"Holmer, M., de Bruyn Kops, C., Stork, C., and Kirchmair, J. (2021). CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates. Molecules, 26.","DOI":"10.3390\/molecules26154678"},{"key":"ref_244","doi-asserted-by":"crossref","unstructured":"Plonka, W., Stork, C., \u0160\u00edcho, M., and Kirchmair, J. (2021). CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. Bioorg. Med. Chem., 46.","DOI":"10.1016\/j.bmc.2021.116388"},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2164-5-20","article-title":"Universal reference RNA as a standard for microarray experiments","volume":"5","author":"Novoradovskaya","year":"2004","journal-title":"BMC Genom."},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-023-02359-w","article-title":"A multi-omics dataset of human transcriptome and proteome stable reference","volume":"10","author":"Lu","year":"2023","journal-title":"Sci. Data"},{"key":"ref_247","first-page":"4971","article-title":"Guidance on the use of the weight of evidence approach in scientific assessments","volume":"15","author":"Hardy","year":"2017","journal-title":"EFSA J."},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"105060","DOI":"10.1016\/j.envint.2019.105060","article-title":"Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy","volume":"131","author":"Benfenati","year":"2019","journal-title":"Environ. Int."},{"key":"ref_249","doi-asserted-by":"crossref","unstructured":"OECD (2024, January 14). Users\u2019 Handbook Supplement to the Guidance Document for Developing and Assessing Adverse Outcome Pathways. Available online: https:\/\/doi.org\/10.1787\/5jlv1m9d1g32-en.","DOI":"10.1787\/5jlv1m9d1g32-en"},{"key":"ref_250","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.yrtph.2016.02.003","article-title":"Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity","volume":"77","author":"Ahlberg","year":"2016","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.yrtph.2017.08.008","article-title":"A consortium-driven framework to guide the implementation of ICH M7 Option 4 control strategies","volume":"90","author":"Barber","year":"2017","journal-title":"Regul. Toxicol. Pharmacol."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/25\/20\/11154\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:15:06Z","timestamp":1760112906000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/25\/20\/11154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"references-count":251,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["ijms252011154"],"URL":"https:\/\/doi.org\/10.3390\/ijms252011154","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,17]]}}}