{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:15:28Z","timestamp":1789395328925,"version":"build-2803163510"},"reference-count":65,"publisher":"American Chemical Society (ACS)","issue":"17","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Traditional carcinogenicity assessment relies on animal experiments, which are costly, time-consuming, and difficult to extrapolate across species. Consequently, the carcinogenic potential of most chemical pollutants remains uncharacterized, posing significant risks to human health. Here, we combined 2356 compounds from public databases with six molecular fingerprints, five base machine learning models, and ensemble strategies to develop PolluCarc-MFSE (a multifingerprint stacking ensemble learning model using Klekota-Roth, extended-connectivity fingerprint (ECFP), and MACCS (Molecular ACCess System) fingerprints) as the optimal predictive model. This model was then applied to predict the carcinogenicity of 179 petrochemical pollutants with unknown carcinogenic potential, as listed in the emission standard of pollutants for the petroleum chemistry industry (GB 31571-2015, 2024 amendment). Among 213 carcinogenic compounds (130 predicted by the model and 83 identified via dataset intersection), the major types were high-ring-number polycyclic aromatic hydrocarbons, halogenated unsaturated hydrocarbons, and multisubstituted aromatics. To assess biological relevance, disease-associated targets for ten pollutant-linked cancers were retrieved from GeneCards. Using a novel multimetric scoring approach in network analysis, we identified 29 hub targets (e.g., JAK2, EGFR, TP53, MYC) that exhibit stable binding to the predicted carcinogens. Pathway enrichment confirmed involvement in cancer-related, PI3K-Akt, and JAK-STAT signaling pathways, while Disease Ontology linked these targets to liver, breast, and lung malignancies. This integrated \u201cdata\u2013model\u2013mechanism\u201d framework provides a practical tool for prioritizing high-risk chemical pollutants and demonstrates how computational toxicology can support regulatory decision-making and the adoption of New Approach Methodologies in chemical risk assessment.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01404","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T22:51:17Z","timestamp":1788303077000},"page":"10702-10719","source":"Crossref","is-referenced-by-count":0,"title":["PolluCarc-MFSE:\nA Multi-Fingerprint Stacking Ensemble\nLearning Model for Chemical Pollutant Carcinogenicity"],"prefix":"10.1021","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2980-5089","authenticated-orcid":true,"given":"Xue-Jiao","family":"Zi","sequence":"first","affiliation":[{"name":"Xinjiang University , , Xinjiang , ,","place":["Urumqi, PR China, 830046"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4695-8535","authenticated-orcid":true,"given":"Zi-Yong","family":"Chu","sequence":"additional","affiliation":[{"name":"Xinjiang University , , Xinjiang , ,","place":["Urumqi, PR China, 830046"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9341-2236","authenticated-orcid":true,"given":"Yu-Long","family":"Li","sequence":"additional","affiliation":[{"name":"Youjiang Medical University for Nationalities , , Guangxi , ,","place":["Baise, PR China, 533000"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"2026091409075382500_cit1","doi-asserted-by":"publisher","first-page":"123955","DOI":"10.1016\/j.envpol.2024.123955","article-title":"Estimates of the\nGlobal Burden of Cancer-Related Deaths Attributable to Residential\nExposure to Petrochemical Industrial Complexes from 2020 to 2040","volume":"350","author":"Boonhat","year":"2024","journal-title":"Environ. Pollut."},{"issue":"10","key":"2026091409075382500_cit2","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1056\/NEJMra2300476","article-title":"Health\nEffects of Fossil Fuel\u2013Derived Endocrine Disruptors","volume":"390","author":"Woodruff","year":"2024","journal-title":"N. Engl. J. Med."},{"issue":"1","key":"2026091409075382500_cit3","doi-asserted-by":"publisher","DOI":"10.5812\/jjcdc-164263","article-title":"Polycyclic Aromatic Hydrocarbons Exposure and Its Impact\non Increased Carcinogenic Risk and COPD: A Review","volume":"15","author":"Moradi","year":"2025","journal-title":"Jundishapur J. Chronic Dis. Care"},{"issue":"5","key":"2026091409075382500_cit4","doi-asserted-by":"publisher","first-page":"543","DOI":"10.3390\/atmos16050543","article-title":"Analysis of VOC\nEmission Characteristics\nin the Petrochemical Industry and Study on Fenceline Monitoring Techniques","volume":"16","author":"Lee","year":"2025","journal-title":"Atmosphere"},{"key":"2026091409075382500_cit5","doi-asserted-by":"publisher","first-page":"114687","DOI":"10.1016\/j.fct.2024.114687","article-title":"Network Toxicology\nand Molecular Docking for the Toxicity Analysis\nof Food Contaminants: A Case of Aflatoxin B1","volume":"188","author":"Chu","year":"2024","journal-title":"Food Chem. Toxicol."},{"key":"2026091409075382500_cit6","doi-asserted-by":"publisher","first-page":"105311","DOI":"10.1016\/j.yrtph.2022.105311","article-title":"Understanding\nand Overcoming the\nTechnical Challenges in Using in Silico Predictions in Regulatory\nDecisions of Complex Toxicological Endpoints \u2013 a Pesticide\nPerspective for Regulatory Toxicologists with a Focus on Machine Learning\nModels","volume":"137","author":"Burgoon","year":"2023","journal-title":"Regul. Toxicol. Pharmacol."},{"key":"2026091409075382500_cit7","doi-asserted-by":"publisher","first-page":"123125","DOI":"10.1016\/j.envres.2025.123125","article-title":"Reassessing\nSHAP-Based Interpretations in QSAR: Model-Centric Limits and Unsupervised\nAlternatives for Fluorocarbon Inhalation Toxicity","volume":"287","author":"Takefuji","year":"2025","journal-title":"Environ. Res."},{"key":"2026091409075382500_cit8","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.procs.2023.11.023","article-title":"Optimized Algorithm Design for Text\nSimilarity Detection Based on Artificial Intelligence and Natural\nLanguage Processing","volume":"228","author":"Liu","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"2026091409075382500_cit9","doi-asserted-by":"publisher","first-page":"arXiv: 2511.17078","DOI":"10.48550\/arXiv.2511.17078","article-title":"Hash Collisions in\nMolecular Fingerprints: Effects on Property Prediction\nand Bayesian Optimization","author":"Virany","year":"2025","journal-title":"arXiv"},{"issue":"8","key":"2026091409075382500_cit10","doi-asserted-by":"publisher","first-page":"2245","DOI":"10.1002\/etc.3744","article-title":"A Re-Evaluation of PETROTOX for Predicting Acute and\nChronic Toxicity of Petroleum Substances","volume":"36","author":"Redman","year":"2017","journal-title":"Environ.\nToxicol. Chem."},{"issue":"1","key":"2026091409075382500_cit11","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1177\/026119290603400119","article-title":"Animal Carcinogenicity Studies: 3.\nAlternatives to the Bioassay","volume":"34","author":"Knight","year":"2006","journal-title":"Altern. Lab. Anim."},{"issue":"3","key":"2026091409075382500_cit12","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s00216-024-05424-4","article-title":"Progress,\nApplications, and Challenges in High-Throughput Effect-Directed Analysis\nfor Toxicity Driver Identification\u2500Is It Time for HT-EDA?","volume":"417","author":"Alvarez-Mora","year":"2025","journal-title":"Anal. Bioanal. Chem."},{"key":"2026091409075382500_cit13","unstructured":"U.S. Food and Drug Administration.\n          .  FDA Announces\nPlan to Phase Out Animal Testing\nRequirement for Monoclonal Antibodies and Other Drugs, 2025."},{"issue":"1","key":"2026091409075382500_cit14","doi-asserted-by":"publisher","first-page":"2611210","DOI":"10.1080\/26895293.2025.2611210","article-title":"Decoding DNA\nDamage: Artificial Intelligence and the Future of Genotoxicity Assessment","volume":"19","author":"Barghi","year":"2026","journal-title":"All Life"},{"issue":"4","key":"2026091409075382500_cit15","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.3390\/jox14040101","article-title":"Overview\nof Computational Toxicology Methods Applied in Drug and Green Chemical\nDiscovery","volume":"14","author":"Bueso-Bordils","year":"2024","journal-title":"J. Xenobiot."},{"issue":"8","key":"2026091409075382500_cit16","doi-asserted-by":"publisher","first-page":"1163","DOI":"10.1021\/acs.chemrestox.3c00217","article-title":"Introduction to the Special Issue: AI Meets Toxicology","volume":"36","author":"Klambauer","year":"2023","journal-title":"Chem. Res. Toxicol."},{"key":"2026091409075382500_cit17","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1016\/j.jes.2025.09.045","article-title":"Deep Learning\nAdvances High-Throughput Toxicity Screening of Chemicals at Multi-Biological\nLevels","volume":"164","author":"Wu","year":"2026","journal-title":"J. Environ. Sci."},{"key":"2026091409075382500_cit18","doi-asserted-by":"publisher","first-page":"109480","DOI":"10.1016\/j.compbiomed.2024.109480","article-title":"ToxSTK: A\nMulti-Target Toxicity Assessment Utilizing Molecular Structure and\nStacking Ensemble Learning","volume":"185","author":"Boonsom","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"8","key":"2026091409075382500_cit19","doi-asserted-by":"publisher","first-page":"101263","DOI":"10.1016\/j.jpha.2025.101263","article-title":"Predicting Cardiotoxicity\nin Drug Development: A Deep Learning Approach","volume":"15","author":"Liu","year":"2025","journal-title":"J. Pharm. Anal."},{"issue":"1","key":"2026091409075382500_cit20","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.1038\/s41598-017-02365-0","article-title":"CarcinoPred-EL: Novel\nModels for Predicting the Carcinogenicity of Chemicals Using Molecular\nFingerprints and Ensemble Learning Methods","volume":"7","author":"Zhang","year":"2017","journal-title":"Sci. Rep."},{"issue":"19","key":"2026091409075382500_cit21","doi-asserted-by":"publisher","first-page":"10194","DOI":"10.1021\/acs.jcim.5c01873","article-title":"Toward Explainable\nCarcinogenicity Prediction: An Integrated\nCheminformatics Approach and Consensus Framework for Possibly Carcinogenic\nChemicals","volume":"65","author":"Duy","year":"2025","journal-title":"J. Chem. Inf. Model."},{"issue":"7\u20138","key":"2026091409075382500_cit22","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1080\/10937404.2019.1642601","article-title":"Development\nof a Database on Tumors and Tumor Sites in Humans and in Experimental\nAnimals for Group 1 Agents Identified through Volume 109 of the IARC\nMonographs","volume":"22","author":"Grosse","year":"2019","journal-title":"J. Toxicol. Environ. Health, Part\nB"},{"issue":"D1","key":"2026091409075382500_cit23","doi-asserted-by":"publisher","first-page":"D1516","DOI":"10.1093\/nar\/gkae1059","article-title":"PubChem 2025 Update","volume":"53","author":"Kim","year":"2025","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026091409075382500_cit24","doi-asserted-by":"publisher","first-page":"D1432","DOI":"10.1093\/nar\/gkac1074","article-title":"TOXRIC: A Comprehensive Database of Toxicological Data and Benchmarks","volume":"51","author":"Wu","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"2026091409075382500_cit25","doi-asserted-by":"publisher","first-page":"114182","DOI":"10.1016\/j.fct.2023.114182","article-title":"Update of the Cancer Potency Database\n(CPDB) to Enable Derivations of Thresholds of Toxicological Concern\n(TTC) for Cancer Potency","volume":"182","author":"Yang","year":"2023","journal-title":"Food Chem. Toxicol."},{"issue":"24","key":"2026091409075382500_cit26","doi-asserted-by":"publisher","first-page":"9027","DOI":"10.1021\/acs.jcim.4c00863","article-title":"MolPipeline:\nA Python Package for\nProcessing Molecules with RDKit in Scikit-Learn","volume":"64","author":"Sieg","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"2026091409075382500_cit27","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13321-020-00456-1","article-title":"An Open\nSource Chemical Structure Curation Pipeline Using RDKit","volume":"12","author":"Bento","year":"2020","journal-title":"J. Cheminf."},{"key":"2026091409075382500_cit28","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.patrec.2019.10.029","article-title":"Auto-Detection\nof Epileptic Seizure Events Using Deep\nNeural Network with Different Feature Scaling Techniques","volume":"128","author":"Thara","year":"2019","journal-title":"Pattern Recognit. Lett."},{"issue":"60","key":"2026091409075382500_cit29","doi-asserted-by":"publisher","first-page":"3021","DOI":"10.21105\/joss.03021","article-title":"Seaborn: Statistical\nData Visualization","volume":"6","author":"Waskom","year":"2021","journal-title":"J. Open Source Softw."},{"issue":"6","key":"2026091409075382500_cit30","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/0898-5529(90)90164-4","article-title":"Database\nStructure\nand Searching in MACCS-3D","volume":"3","author":"Christie","year":"1990","journal-title":"Tetrahedron Comput.\nMethodol."},{"key":"2026091409075382500_cit31","doi-asserted-by":"publisher","first-page":"137575","DOI":"10.1016\/j.jhazmat.2025.137575","article-title":"Web Server-Based Deep Learning-Driven\nPredictive Models for Respiratory\nToxicity of Environmental Chemicals: Mechanistic Insights and Interpretability","volume":"489","author":"Li","year":"2025","journal-title":"J. Hazard. Mater."},{"issue":"5","key":"2026091409075382500_cit32","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1021\/ci900043r","article-title":"APIF: A New\nInteraction Fingerprint Based on Atom Pairs and Its Application to\nVirtual Screening","volume":"49","author":"Pe\u0301rez-Nueno","year":"2009","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"2026091409075382500_cit33","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/s13321-018-0321-8","article-title":"A Probabilistic\nMolecular Fingerprint for Big Data\nSettings","volume":"10","author":"Probst","year":"2018","journal-title":"J. Cheminf."},{"issue":"4","key":"2026091409075382500_cit34","doi-asserted-by":"publisher","first-page":"3165","DOI":"10.1007\/s11030-024-11066-6","article-title":"HDAC3_VS_assistant: Cheminformatics-Driven\nDiscovery\nof Histone Deacetylase 3 Inhibitors","volume":"29","author":"Tinkov","year":"2025","journal-title":"Mol. 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Photogramm.\nRemote Sens."},{"issue":"3","key":"2026091409075382500_cit37","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least Squares\nSupport Vector Machine Classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process.\nLett."},{"issue":"2","key":"2026091409075382500_cit38","doi-asserted-by":"publisher","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-Nearest\nNeighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"2026091409075382500_cit39","doi-asserted-by":"crossref","unstructured":"Kamel, H.; Abdulah, D.; Al-Tuwaijari, J. M.\n          Cancer Classification\nUsing Gaussian Naive Bayes Algorithm. In  2019\nInternational Engineering Conference (IEC); IEEE: Erbil, Iraq, pp 165\u2013170.10.1109\/IEC47844.2019.8950650.","DOI":"10.1109\/IEC47844.2019.8950650"},{"key":"2026091409075382500_cit40","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.ijcce.2021.01.001","article-title":"An Ensemble Approach\nfor Classification\nand Prediction of Diabetes Mellitus Using Soft Voting Classifier","volume":"2","author":"Kumari","year":"2021","journal-title":"Int. J. Cognit. Comput. Eng."},{"key":"2026091409075382500_cit41","doi-asserted-by":"publisher","first-page":"103386","DOI":"10.1016\/j.eclinm.2025.103386","article-title":"Development\nand Validation of an Explainable Machine Learning Model for Predicting\nPostoperative Pulmonary Complications after Lung Cancer Surgery: A\nMachine Learning Study","volume":"86","author":"Chen","year":"2025","journal-title":"eClinicalMedicine"},{"key":"2026091409075382500_cit42","doi-asserted-by":"publisher","first-page":"606668","DOI":"10.3389\/fphar.2020.606668","article-title":"Improvement\nof Prediction\nPerformance with Conjoint Molecular Fingerprint in Deep Learning","volume":"11","author":"Xie","year":"2020","journal-title":"Front. Pharmacol."},{"key":"2026091409075382500_cit43","doi-asserted-by":"publisher","first-page":"112128","DOI":"10.1016\/j.diabres.2025.112128","article-title":"Association\nbetween\nDifferent Triglyceride Glucose Index-Related Indicators and Overactive\nBladder","volume":"223","author":"Huang","year":"2025","journal-title":"Diabetes Res. Clin. Pract."},{"issue":"1","key":"2026091409075382500_cit44","doi-asserted-by":"publisher","first-page":"1.30.1","DOI":"10.1002\/cpbi.5","article-title":"The GeneCards Suite: From Gene Data\nMining to Disease Genome Sequence Analyses","volume":"54","author":"Stelzer","year":"2016","journal-title":"Curr. Protoc. Bioinform."},{"issue":"D1","key":"2026091409075382500_cit45","doi-asserted-by":"publisher","first-page":"D638","DOI":"10.1093\/nar\/gkac1000","article-title":"The STRING\nDatabase in 2023: Protein\u2013Protein Association Networks and\nFunctional Enrichment Analyses for Any Sequenced Genome of Interest","volume":"51","author":"Szklarczyk","year":"2023","journal-title":"Nucleic Acids Res."},{"key":"2026091409075382500_cit46","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/978-1-0716-3327-4_35","article-title":"Protein\u2013Protein\nInteraction Network Analysis Using NetworkX","volume":"2690","author":"Hasan","year":"2023","journal-title":"Methods Mol. Biol."},{"issue":"13","key":"2026091409075382500_cit47","doi-asserted-by":"publisher","first-page":"2214","DOI":"10.1093\/bioinformatics\/btv082","article-title":"Fast, Accurate,\nand Reliable Molecular Docking with QuickVina 2","volume":"31","author":"Alhossary","year":"2015","journal-title":"Bioinformatics"},{"issue":"D1","key":"2026091409075382500_cit48","doi-asserted-by":"publisher","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","article-title":"AlphaFold Protein Structure Database:\nMassively Expanding the Structural Coverage of Protein-Sequence Space\nwith High-Accuracy Models","volume":"50","author":"Varadi","year":"2022","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"2026091409075382500_cit49","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1038\/nmeth.4067","article-title":"CHARMM36m:\nAn Improved Force Field for Folded and Intrinsically Disordered Proteins","volume":"14","author":"Huang","year":"2017","journal-title":"Nat. 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Pap."},{"issue":"18","key":"2026091409075382500_cit51","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.3390\/plants14182828","article-title":"Network Pharmacology,\nMolecular Docking\nand Molecular Dynamics Studies to Predict the Molecular Targets and\nMechanisms of Action of Melissa Officinalis Phytoconstituents in Type-2\nDiabetes Mellitus","volume":"14","author":"Ononamadu","year":"2025","journal-title":"Plants"},{"issue":"3","key":"2026091409075382500_cit52","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1080\/15548627.2017.1415189","article-title":"Exploring Autophagy with Gene Ontology","volume":"14","author":"Denny","year":"2018","journal-title":"Autophagy"},{"issue":"D1","key":"2026091409075382500_cit53","doi-asserted-by":"publisher","first-page":"D353","DOI":"10.1093\/nar\/gkw1092","article-title":"KEGG: New\nPerspectives on Genomes, Pathways, Diseases and Drugs","volume":"45","author":"Kanehisa","year":"2017","journal-title":"Nucleic Acids Res."},{"issue":"D1","key":"2026091409075382500_cit54","doi-asserted-by":"publisher","first-page":"D1255","DOI":"10.1093\/nar\/gkab1063","article-title":"The Human Disease Ontology 2022 Update","volume":"50","author":"Schriml","year":"2022","journal-title":"Nucleic Acids Res."},{"issue":"7","key":"2026091409075382500_cit55","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.tips.2023.04.002","article-title":"Advancing Chemical Carcinogenicity Prediction Modeling: Opportunities\nand Challenges","volume":"44","author":"Mittal","year":"2023","journal-title":"Trends Pharmacol. Sci."},{"key":"2026091409075382500_cit56","doi-asserted-by":"publisher","first-page":"114466","DOI":"10.1016\/j.ecoenv.2022.114466","article-title":"Toxicogenomics\nScoring System: TGSS, a Novel Integrated Risk Assessment Model for\nChemical Carcinogenicity Prediction","volume":"250","author":"Lu","year":"2023","journal-title":"Ecotoxicol.\nEnviron. Saf."},{"issue":"16","key":"2026091409075382500_cit57","doi-asserted-by":"publisher","first-page":"6573","DOI":"10.1021\/acs.est.3c00648","article-title":"Data-Driven Quantitative Structure\u2013Activity\nRelationship Modeling for Human Carcinogenicity by Chronic Oral Exposure","volume":"57","author":"Chung","year":"2023","journal-title":"Environ. Sci. Technol."},{"issue":"7","key":"2026091409075382500_cit58","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1021\/acs.jcim.3c00692","article-title":"VenomPred 2.0: A Novel In Silico Platform\nfor an Extended and Human Interpretable Toxicological Profiling of\nSmall Molecules","volume":"64","author":"Di Stefano","year":"2024","journal-title":"J. Chem. Inf. Model."},{"issue":"41","key":"2026091409075382500_cit59","doi-asserted-by":"publisher","first-page":"21830","DOI":"10.1021\/acs.est.5c04614","article-title":"Multiscale-Information-Embedded\nUniversal Toxicity Prediction Framework","volume":"59","author":"Wu","year":"2025","journal-title":"Environ.\nSci. 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