{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:51:19Z","timestamp":1785426679565,"version":"3.56.0"},"reference-count":191,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":36,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471071"],"award-info":[{"award-number":["62471071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202069"],"award-info":[{"award-number":["62202069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chengdu Health Commission-Chengdu University of Traditional Chinese Medicine Joint Research Fund","award":["WXLH202402041"],"award-info":[{"award-number":["WXLH202402041"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Toxicity risk assessment plays a crucial role in determining the clinical success and market potential of drug candidates. Traditional animal-based testing is costly, time-consuming, and ethically controversial, which has led to the rapid development of computational toxicology. This review surveys over 20 ADMET prediction platforms, categorizing them into rule\/statistical-based methods, machine learning (ML) methods, and graph-based methods. We also summarize major toxicological databases into four types: chemical toxicity, environmental toxicology, alternative toxicology, and biological toxin databases, highlighting their roles in model training and validation. Furthermore, we review recent advancements in ML and artificial intelligence (AI) applied to toxicity prediction, covering acute toxicity, organ-specific toxicities, and carcinogenicity. The field is transitioning from single-endpoint predictions to multi-endpoint joint modeling, incorporating multimodal features. We also explore the application of generative modeling techniques and interpretability frameworks to improve the accuracy and credibility of predictions. Additionally, we discuss the use of network toxicology in evaluating the safety of traditional Chinese medicines (TCMs) and the potential of large language models (LLMs) in literature mining, knowledge integration, and molecular toxicity prediction. Finally, we address current challenges, including data quality, model interpretability, and causal inference, and propose future directions such as multi-omics integration, interpretable AI models, and domain-specific LLMs, aiming to provide more efficient and precise technical support for preclinical toxicity assessments in drug development.<\/jats:p>","DOI":"10.1093\/bib\/bbaf533","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T12:02:33Z","timestamp":1758283353000},"source":"Crossref","is-referenced-by-count":72,"title":["Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction"],"prefix":"10.1093","volume":"26","author":[{"given":"Jiangyan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Pharmacy\/School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine , No. 1166, Liutai Avenue, Wenjiang District, Chengdu City, Sichuan Province, 611137 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haolin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine , No. 1166, Liutai Avenue, Wenjiang District, Chengdu City, Sichuan Province, 611137 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuncong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University) , No. 79, Kangning Road, Xiangzhou District, Zhuhai City, Guangdong Province, 519000 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyang","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China , No. 32, Section 2, West Yihuan Road, Qingyang District, Chengdu, Sichuan Province, 610072 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Healthcare Technology, Chengdu Neusoft University , No. 1, Neusoft Avenue, Qingchengshan Town, Dujiangyan City, Chengdu, Sichuan Province, 611844 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuantao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine, Hospital of Chengdu University of Traditional Chinese Medicine , No. 39, Shi'erqiao Road, Jinniu District, Chengdu, Sichuan Province, 610072 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , No. 2006, Xiyuan Avenue, High-tech Zone (West Zone), Chengdu, Sichuan Province, 611731 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1317-120X","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine , No. 1166, Liutai Avenue, Wenjiang District, Chengdu City, Sichuan Province, 611137 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,10,6]]},"reference":[{"key":"2025122308564817300_ref1","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1038\/nrd3405","article-title":"The productivity crisis in pharmaceutical R&D","volume":"10","author":"Pammolli","year":"2011","journal-title":"Nat Rev Drug Discov"},{"key":"2025122308564817300_ref2","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/d41573-019-00074-z","article-title":"Trends in clinical success rates and therapeutic focus","volume":"18","author":"Dowden","year":"2019","journal-title":"Nat Rev Drug Discov"},{"key":"2025122308564817300_ref3","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1021\/acs.jcim.3c00200","article-title":"Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives","volume":"63","author":"Tran","year":"2023","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref4","doi-asserted-by":"publisher","first-page":"13","DOI":"10.2174\/1573406412666160229150803","article-title":"Cell-based assays for assessing toxicity: A basic guide","volume":"13","author":"Parboosing","year":"2016","journal-title":"Med Chem"},{"key":"2025122308564817300_ref5","doi-asserted-by":"publisher","first-page":"153053","DOI":"10.1016\/j.tox.2021.153053","article-title":"Alternative animal models in predictive toxicology","volume":"465","author":"Khabib","year":"2022","journal-title":"Toxicology"},{"key":"2025122308564817300_ref6","doi-asserted-by":"publisher","first-page":"104195","DOI":"10.1016\/j.drudis.2024.104195","article-title":"Data-driven toxicity prediction in drug discovery: current status and future directions","volume":"29","author":"Wang","year":"2024","journal-title":"Drug Discov Today"},{"key":"2025122308564817300_ref7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ddmod.2017.11.005","article-title":"Overview of 3Rs opportunities in drug discovery and development using non-human primates","volume":"23","author":"Prior","year":"2017","journal-title":"Drug Discov Today Dis Model"},{"key":"2025122308564817300_ref8","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.vascn.2013.12.003","article-title":"Progress in computational toxicology","volume":"69","author":"Ekins","year":"2014","journal-title":"J Pharmacol Toxicol Methods"},{"key":"2025122308564817300_ref9","article-title":"Integrated multi-omics analyses reveal lipid metabolic signature in osteoarthritis","volume":"437","author":"Wang","journal-title":"J Mol Biol"},{"key":"2025122308564817300_ref10","first-page":"109713","volume":"27","author":"Meng","year":"2024","journal-title":"The application of large language models in medicine: A scoping review, iScience"},{"key":"2025122308564817300_ref11","doi-asserted-by":"publisher","first-page":"100122","DOI":"10.1016\/j.crstbi.2023.100122","article-title":"SAGESDA: multi-GraphSAGE networks for predicting SnoRNA-disease associations","volume":"7","author":"Momanyi","year":"2024","journal-title":"Curr Res Struct Biol"},{"key":"2025122308564817300_ref12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2133\/dmpk.DMPK-10-RV-062","article-title":"Mechanisms of drug toxicity and relevance to pharmaceutical development","volume":"26","author":"Guengerich","year":"2011","journal-title":"Drug Metab Pharmacokinet"},{"key":"2025122308564817300_ref13","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad487","article-title":"Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning","volume":"25","author":"Liu","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025122308564817300_ref14","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1021\/acs.chemrestox.5b00465","article-title":"Computational models for human and animal hepatotoxicity with a global application scope","volume":"29","author":"Mulliner","year":"2016","journal-title":"Chem Res Toxicol"},{"key":"2025122308564817300_ref15","doi-asserted-by":"crossref","first-page":"1441587","DOI":"10.3389\/fphar.2024.1441587","article-title":"Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning","volume":"15","author":"Khan","year":"2024","journal-title":"Front Pharmacol"},{"key":"2025122308564817300_ref16","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1080\/17425255.2024.2377593","article-title":"Machine learning and deep learning approaches for enhanced prediction of hERG blockade: A comprehensive QSAR modeling study","volume":"20","author":"Liu","year":"2024","journal-title":"Expert Opin Drug Metab Toxicol"},{"key":"2025122308564817300_ref17","article-title":"Trends of artificial intelligence (AI) use in drug targets, discovery and development: current status and future perspectives","volume":"26","author":"Mahapatra","journal-title":"Curr Drug Targets"},{"key":"2025122308564817300_ref18","article-title":"Attention is all you need: utilizing attention in AI-enabled drug discovery","volume":"25","author":"Zhang","journal-title":"Brief Bioinform"},{"key":"2025122308564817300_ref19","article-title":"Deep-STP: A deep learning-based approach to predict snake toxin proteins by using word embeddings","volume":"10","author":"Zulfiqar","year":"2024","journal-title":"Front Med (Lausanne)"},{"key":"2025122308564817300_ref20","doi-asserted-by":"publisher","first-page":"987","DOI":"10.2174\/1568026618666180727152557","article-title":"Applications of machine learning methods in drug toxicity prediction","volume":"18","author":"Zhang","year":"2018","journal-title":"Curr Top Med Chem"},{"key":"2025122308564817300_ref21","doi-asserted-by":"publisher","first-page":"6924","DOI":"10.1021\/acs.jmedchem.1c00421","article-title":"Mining toxicity information from large amounts of toxicity data","volume":"64","author":"Wu","year":"2021","journal-title":"J Med Chem"},{"key":"2025122308564817300_ref22","doi-asserted-by":"publisher","first-page":"7617","DOI":"10.1021\/acs.jcim.3c01642","article-title":"From black boxes to actionable insights: A perspective on explainable artificial intelligence for scientific discovery","volume":"63","author":"Wu","year":"2023","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref23","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.1177\/15353702231209421","article-title":"Review of machine learning and deep learning models for toxicity prediction","volume":"248","author":"Guo","year":"2023","journal-title":"Exp Biol Med (Maywood)"},{"key":"2025122308564817300_ref24","first-page":"303","article-title":"Advancing predictive toxicology: overcoming hurdles and shaping the future","volume":"4","author":"Masarone","year":"2024","journal-title":"Dig Dis"},{"key":"2025122308564817300_ref25","doi-asserted-by":"crossref","first-page":"4908","DOI":"10.1038\/s41598-023-31169-8","article-title":"Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations","volume":"13","author":"Sharma","year":"2023","journal-title":"Sci Rep"},{"key":"2025122308564817300_ref26","doi-asserted-by":"publisher","first-page":"129361","DOI":"10.1016\/j.jhazmat.2022.129361","article-title":"Absorption, distribution, metabolism, excretion and toxicity of microplastics in the human body and health implications","volume":"437","author":"Wu","year":"2022","journal-title":"J Hazard Mater"},{"key":"2025122308564817300_ref27","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/nrd4609","article-title":"An analysis of the attrition of drug candidates from four major pharmaceutical companies","volume":"14","author":"Waring","year":"2015","journal-title":"Nat Rev Drug Discov"},{"key":"2025122308564817300_ref28","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.yrtph.2016.07.011","article-title":"The acute lethal dose 50 (LD50) of caffeine in albino rats","volume":"80","author":"Adamson","year":"2016","journal-title":"Regul Toxicol Pharmacol"},{"key":"2025122308564817300_ref29","doi-asserted-by":"crossref","first-page":"bbae008","DOI":"10.1093\/bib\/bbae008","article-title":"ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization","volume":"25","author":"Yi","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025122308564817300_ref30","doi-asserted-by":"crossref","first-page":"42717","DOI":"10.1038\/srep42717","article-title":"SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules","volume":"7","author":"Daina","year":"2017","journal-title":"Sci Rep"},{"key":"2025122308564817300_ref31","doi-asserted-by":"publisher","first-page":"W432","DOI":"10.1093\/nar\/gkae298","article-title":"admetSAR3.0: A comprehensive platform for exploration, prediction and optimization of chemical ADMET properties","volume":"52","author":"Gu","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref32","doi-asserted-by":"publisher","first-page":"W422","DOI":"10.1093\/nar\/gkae236","article-title":"ADMETlab 3.0: an updated comprehensive online ADMET prediction platform enhanced with broader coverage, improved performance, API functionality and decision support","volume":"52","author":"Fu","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref33","doi-asserted-by":"crossref","first-page":"889","DOI":"10.3389\/fphar.2017.00889","article-title":"vNN web server for ADMET predictions","volume":"8","author":"Schyman","year":"2017","journal-title":"Front Pharmacol"},{"key":"2025122308564817300_ref34","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1007\/s00894-022-05373-8","article-title":"ADMETboost: A web server for accurate ADMET prediction","volume":"28","author":"Tian","year":"2022","journal-title":"J Mol Model"},{"key":"2025122308564817300_ref35","doi-asserted-by":"publisher","first-page":"3658","DOI":"10.1093\/bioinformatics\/btx491","article-title":"FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery","volume":"33","author":"Lagorce","year":"2017","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref36","doi-asserted-by":"publisher","DOI":"10.1101\/2023.12.28.573531","article-title":"ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries","volume":"40","author":"Swanson","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref37","doi-asserted-by":"publisher","first-page":"W513","DOI":"10.1093\/nar\/gkae303","article-title":"ProTox 3.0: A webserver for the prediction of toxicity of chemicals","volume":"52","author":"Banerjee","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref38","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1021\/acs.jcim.3c00692","article-title":"VenomPred 2.0: A novel In Silico platform for an extended and human interpretable toxicological profiling of small molecules","volume":"64","author":"Di Stefano","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref39","doi-asserted-by":"publisher","first-page":"1869","DOI":"10.1093\/bioinformatics\/btv043","article-title":"CypRules: A rule-based P450 inhibition prediction server","volume":"31","author":"Shao","year":"2015","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref40","doi-asserted-by":"publisher","first-page":"W115","DOI":"10.1093\/nar\/gkac313","article-title":"BioTransformer 3.0-a web server for accurately predicting metabolic transformation products","volume":"50","author":"Wishart","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref41","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1093\/bioinformatics\/btu761","article-title":"XenoSite server: A web-available site of metabolism prediction tool","volume":"31","author":"Matlock","year":"2015","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref42","doi-asserted-by":"publisher","first-page":"2046","DOI":"10.1093\/bioinformatics\/btv087","article-title":"SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds","volume":"31","author":"Rudik","year":"2015","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref43","doi-asserted-by":"publisher","first-page":"3174","DOI":"10.1093\/bioinformatics\/btz037","article-title":"SMARTCyp 3.0: enhanced cytochrome P450 site-of-metabolism prediction server","volume":"35","author":"Olsen","year":"2019","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref44","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.jare.2023.10.012","article-title":"P450Rdb: A manually curated database of reactions catalyzed by cytochrome P450 enzymes","volume":"63","author":"Zhang","year":"2024","journal-title":"J Adv Res"},{"key":"2025122308564817300_ref45","doi-asserted-by":"crossref","first-page":"1281880","DOI":"10.3389\/fmed.2023.1281880","article-title":"Accurately identifying hemagglutinin using sequence information and machine learning methods","volume":"10","author":"Zou","year":"2023","journal-title":"Front Med (Lausanne)"},{"key":"2025122308564817300_ref46","doi-asserted-by":"publisher","first-page":"105911","DOI":"10.1016\/j.compbiomed.2022.105911","article-title":"FRTpred: A novel approach for accurate prediction of protein folding rate and type","volume":"149","author":"Manavalan","year":"2022","journal-title":"Comput Biol Med"},{"key":"2025122308564817300_ref47","doi-asserted-by":"publisher","first-page":"133085","DOI":"10.1016\/j.ijbiomac.2024.133085","article-title":"SEP-AlgPro: an efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features","volume":"273","author":"Basith","year":"2024","journal-title":"Int J Biol Macromol"},{"key":"2025122308564817300_ref48","doi-asserted-by":"publisher","first-page":"108859","DOI":"10.1016\/j.compbiomed.2024.108859","article-title":"HOTGpred: enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach","volume":"179","author":"Pham","year":"2024","journal-title":"Comput Biol Med"},{"key":"2025122308564817300_ref49","doi-asserted-by":"publisher","first-page":"W633","DOI":"10.1093\/nar\/gkac415","article-title":"RaacFold: A webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets","volume":"50","author":"Zheng","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref50","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1021\/envhealth.4c00014","article-title":"AquaticTox: A web-based tool for aquatic toxicity evaluation based on ensemble learning to facilitate the screening of green chemicals","volume":"2","author":"Shi","year":"2024","journal-title":"Environ Health (Wash)"},{"key":"2025122308564817300_ref51","doi-asserted-by":"publisher","first-page":"4066","DOI":"10.1021\/acs.jmedchem.5b00104","article-title":"pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures","volume":"58","author":"Pires","year":"2015","journal-title":"J Med Chem"},{"key":"2025122308564817300_ref52","doi-asserted-by":"publisher","first-page":"2863","DOI":"10.1093\/bioinformatics\/btac192","article-title":"Interpretable-ADMET: A web service for ADMET prediction and optimization based on deep neural representation","volume":"38","author":"Wei","year":"2022","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref53","doi-asserted-by":"publisher","first-page":"3444","DOI":"10.1093\/bioinformatics\/btac342","article-title":"HelixADMET: A robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer","volume":"38","author":"Zhang","year":"2022","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref54","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa160","article-title":"Current development of integrated web servers for preclinical safety and pharmacokinetics assessments in drug development","volume":"22","author":"Hsiao","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025122308564817300_ref55","first-page":"75","article-title":"FP-ADMET: A compendium of fingerprint-based ADMET prediction models","volume":"13","author":"Venkatraman","year":"2021","journal-title":"J Chem"},{"key":"2025122308564817300_ref56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/1513503","article-title":"HobPre: accurate prediction of human oral bioavailability for small molecules","volume":"14","author":"Wei","year":"2022","journal-title":"J Chem"},{"key":"2025122308564817300_ref57","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1038\/s41596-023-00942-4","article-title":"OptADMET: A web-based tool for substructure modifications to improve ADMET properties of lead compounds","volume":"19","author":"Yi","year":"2024","journal-title":"Nat Protoc"},{"key":"2025122308564817300_ref58","doi-asserted-by":"publisher","first-page":"W469","DOI":"10.1093\/nar\/gkae254","article-title":"Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction","volume":"52","author":"Myung","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref59","doi-asserted-by":"publisher","first-page":"415","DOI":"10.3109\/15376511003667842","article-title":"Toxicoproteomics: new paradigms in toxicology research","volume":"20","author":"George","year":"2010","journal-title":"Toxicol Mech Methods"},{"key":"2025122308564817300_ref60","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1038\/s41573-025-01164-x","article-title":"Computational drug repurposing: approaches, evaluation of in silico resources and case studies","volume":"24","author":"Tanoli","year":"2025","journal-title":"Nat Rev Drug Discov"},{"key":"2025122308564817300_ref61","doi-asserted-by":"publisher","first-page":"D1388","DOI":"10.1093\/nar\/gkaa971","article-title":"PubChem in 2021: new data content and improved web interfaces","volume":"49","author":"Kim","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref62","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","article-title":"The ChEMBL database in 2023: A drug discovery platform spanning multiple bioactivity data types and time periods","volume":"52","author":"Zdrazil","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref63","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":"2025122308564817300_ref64","doi-asserted-by":"publisher","first-page":"D1265","DOI":"10.1093\/nar\/gkad976","article-title":"DrugBank 6.0: the DrugBank knowledgebase for 2024","volume":"52","author":"Knox","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref65","doi-asserted-by":"publisher","first-page":"D295","DOI":"10.1093\/nar\/gkn850","article-title":"SuperToxic: A comprehensive database of toxic compounds","volume":"37","author":"Schmidt","year":"2009","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref66","first-page":"61","article-title":"The CompTox chemistry dashboard: A community data resource for environmental chemistry","volume":"9","author":"Williams","year":"2017","journal-title":"J Chem"},{"key":"2025122308564817300_ref67","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1300\/J115v21n01_04","article-title":"ChemIDplus-super source for chemical and drug information","volume":"21","author":"Tomasulo","year":"2002","journal-title":"Med Ref Serv Q"},{"key":"2025122308564817300_ref68","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.tox.2014.09.003","article-title":"The National Library of Medicine's (NLM) hazardous substances data Bank (HSDB): background, recent enhancements and future plans","volume":"325","author":"Fonger","year":"2014","journal-title":"Toxicology"},{"key":"2025122308564817300_ref69","doi-asserted-by":"publisher","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":"2025122308564817300_ref70","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.drudis.2019.09.022","article-title":"Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity","volume":"25","author":"Thakkar","year":"2020","journal-title":"Drug Discov Today"},{"key":"2025122308564817300_ref71","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.drudis.2011.05.007","article-title":"FDA-approved drug labeling for the study of drug-induced liver injury","volume":"16","author":"Chen","year":"2011","journal-title":"Drug Discov Today"},{"key":"2025122308564817300_ref72","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1089\/adt.2011.0425","article-title":"hERGCentral: A large database to store, retrieve, and analyze compound-human ether-\u00e0-go-go related gene channel interactions to facilitate cardiotoxicity assessment in drug development","volume":"9","author":"Du","year":"2011","journal-title":"Assay Drug Dev Technol"},{"key":"2025122308564817300_ref73","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1080\/02763860802198895","article-title":"CPDB: carcinogenic potency database","volume":"27","author":"Fitzpatrick","year":"2008","journal-title":"Med Ref Serv Q"},{"key":"2025122308564817300_ref74","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s10822-011-9440-2","article-title":"Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information","volume":"25","author":"Sushko","year":"2011","journal-title":"J Comput Aided Mol Des"},{"key":"2025122308564817300_ref75","doi-asserted-by":"publisher","first-page":"D928","DOI":"10.1093\/nar\/gku1004","article-title":"T3DB: the toxic exposome database","volume":"43","author":"Wishart","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref76","doi-asserted-by":"crossref","first-page":"054501","DOI":"10.1289\/EHP1759","article-title":"An integrated chemical environment to support 21st-century toxicology","volume":"125","author":"Bell","year":"2017","journal-title":"Environ Health Perspect"},{"key":"2025122308564817300_ref77","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref78","doi-asserted-by":"publisher","first-page":"100642","DOI":"10.1016\/j.medj.2025.100642","article-title":"OnSIDES database: extracting adverse drug events from drug labels using natural language processing models","volume":"6","author":"Tanaka","year":"2025","journal-title":"Fortschr Med"},{"key":"2025122308564817300_ref79","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1177\/009286150804200501","article-title":"VigiBase, the WHO global ICSR database system: basic facts","volume":"42","author":"Lindquist","year":"2008","journal-title":"Drug information journal : DIJ \/ Drug Information Association"},{"key":"2025122308564817300_ref80","doi-asserted-by":"publisher","first-page":"4398","DOI":"10.1016\/j.vaccine.2015.07.035","article-title":"Safety monitoring in the vaccine adverse event reporting system (VAERS)","volume":"33","author":"Shimabukuro","year":"2015","journal-title":"Vaccine"},{"key":"2025122308564817300_ref81","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1002\/etc.5324","article-title":"The ECOTOXicology knowledgebase: A curated database of ecologically relevant toxicity tests to support environmental research and risk assessment","volume":"41","author":"Olker","year":"2022","journal-title":"Environ Toxicol Chem"},{"key":"2025122308564817300_ref82","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0300-483X(00)00337-1","article-title":"TOXNET: an evolving web resource for toxicology and environmental health information","volume":"157","author":"Wexler","year":"2001","journal-title":"Toxicology"},{"key":"2025122308564817300_ref83","doi-asserted-by":"publisher","first-page":"1062","DOI":"10.1002\/etc.4382","article-title":"Creation of a curated aquatic toxicology database: EnviroTox","volume":"38","author":"Connors","year":"2019","journal-title":"Environ Toxicol Chem"},{"key":"2025122308564817300_ref84","first-page":"1050","article-title":"An international database for pesticide risk assessments and management, human and ecological risk assessment: an","volume":"22","author":"Lewis","year":"2016","journal-title":"Int J"},{"key":"2025122308564817300_ref85","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s12915-024-01950-w","article-title":"Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb","volume":"22","author":"Zhang","year":"2024","journal-title":"BMC Biol"},{"key":"2025122308564817300_ref86","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.drudis.2013.05.015","article-title":"The Tox21 robotic platform for the assessment of environmental chemicals--from vision to reality","volume":"18","author":"Attene-Ramos","year":"2013","journal-title":"Drug Discov Today"},{"key":"2025122308564817300_ref87","doi-asserted-by":"publisher","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":"2025122308564817300_ref88","doi-asserted-by":"publisher","first-page":"W455","DOI":"10.1093\/nar\/gkaa390","article-title":"ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets","volume":"48","author":"Nair","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref89","doi-asserted-by":"crossref","first-page":"1119923","DOI":"10.3389\/fvets.2023.1119923","article-title":"Norecopa: A global knowledge base of resources for improving animal research and testing","volume":"10","author":"Smith","year":"2023","journal-title":"Front Vet Sci"},{"key":"2025122308564817300_ref90","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1093\/toxsci\/kfq026","article-title":"Comparison of HepG2 and HepaRG by whole-genome gene expression analysis for the purpose of chemical Hazard identification","volume":"115","author":"Jennen","year":"2010","journal-title":"Toxicol Sci"},{"key":"2025122308564817300_ref91","doi-asserted-by":"publisher","first-page":"D1138","DOI":"10.1093\/nar\/gkaa891","article-title":"Comparative Toxicogenomics database (CTD): update 2021","volume":"49","author":"Davis","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref92","doi-asserted-by":"publisher","author":"Wang","DOI":"10.48550\/arXiv.2412.20038"},{"key":"2025122308564817300_ref93","doi-asserted-by":"publisher","first-page":"124810","DOI":"10.1016\/j.jhazmat.2020.124810","article-title":"A data-driven integrative platform for computational prediction of toxin biotransformation with a case study","volume":"408","author":"Zhang","year":"2021","journal-title":"J Hazard Mater"},{"key":"2025122308564817300_ref94","doi-asserted-by":"publisher","first-page":"7577","DOI":"10.1021\/acs.jafc.8b01639","article-title":"Comprehensive toxic plants-Phytotoxins database and its application in assessing aquatic micropollution potential","volume":"66","author":"G\u00fcnthardt","year":"2018","journal-title":"J Agric Food Chem"},{"key":"2025122308564817300_ref95","doi-asserted-by":"publisher","first-page":"110273","DOI":"10.1016\/j.foodcont.2023.110273","article-title":"MycoCentral: an innovative database to compile information on mycotoxins and facilitate hazard prediction","volume":"159","author":"Habauzit","year":"2024","journal-title":"Food Control"},{"key":"2025122308564817300_ref96","doi-asserted-by":"publisher","first-page":"D293","DOI":"10.1093\/nar\/gkm832","article-title":"ATDB: A uni-database platform for animal toxins","volume":"36","author":"He","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref97","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.toxicon.2005.12.001","article-title":"SCORPION2: A database for structure-function analysis of SCORPION toxins","volume":"47","author":"Tan","year":"2006","journal-title":"Toxicon"},{"key":"2025122308564817300_ref98","doi-asserted-by":"crossref","first-page":"D325","DOI":"10.1093\/nar\/gkr886","article-title":"ConoServer: updated content, knowledge, and discovery tools in the conopeptide database","volume":"40","author":"Kaas","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref99","doi-asserted-by":"publisher","first-page":"9501","DOI":"10.1021\/acs.jafc.3c01403","article-title":"MycotoxinDB: A data-driven platform for investigating masked forms of mycotoxins","volume":"71","author":"Ji","year":"2023","journal-title":"J Agric Food Chem"},{"key":"2025122308564817300_ref100","doi-asserted-by":"crossref","first-page":"e0191123","DOI":"10.1128\/mbio.01911-23","article-title":"Toxinome-the bacterial protein toxin database","volume":"15","author":"Danov","year":"2024","journal-title":"MBio"},{"key":"2025122308564817300_ref101","doi-asserted-by":"crossref","first-page":"169783","DOI":"10.1016\/j.scitotenv.2023.169783","article-title":"Predictive value of the ToxCast\/Tox21 high throughput toxicity screening data for approximating in vivo ecotoxicity endpoints and ecotoxicological risk in eco- surveillance applications","volume":"914","author":"Rodea-Palomares","year":"2024","journal-title":"Sci Total Environ"},{"key":"2025122308564817300_ref102","doi-asserted-by":"crossref","first-page":"2400044","DOI":"10.1002\/pmic.202400044","article-title":"Identification of RNA-dependent liquid-liquid phase separation proteins using an artificial intelligence strategy","volume":"24","author":"Ahmed","year":"2024","journal-title":"Proteomics"},{"key":"2025122308564817300_ref103","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1186\/s13040-021-00278-3","article-title":"Prediction of synergistic drug combinations using PCA-initialized deep learning","volume":"14","author":"Ma","year":"2021","journal-title":"BioData Mining"},{"key":"2025122308564817300_ref104","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.future.2024.06.008","article-title":"ACVPred: enhanced prediction of anti-coronavirus peptides by transfer learning combined with data augmentation","volume":"160","author":"Xu","year":"2024","journal-title":"Futur Gener Comput Syst"},{"key":"2025122308564817300_ref105","doi-asserted-by":"crossref","first-page":"2400009","DOI":"10.1002\/advs.202400009","article-title":"CodLncScape provides a self-enriching framework for the systematic collection and exploration of coding LncRNAs","volume":"11","author":"Liu","year":"2024","journal-title":"Adv Sci"},{"key":"2025122308564817300_ref106","doi-asserted-by":"publisher","first-page":"130638","DOI":"10.1016\/j.ijbiomac.2024.130638","article-title":"Cm-siRPred: predicting chemically modified siRNA efficiency based on multi-view learning strategy","volume":"264","author":"Liu","year":"2024","journal-title":"Int J Biol Macromol"},{"key":"2025122308564817300_ref107","doi-asserted-by":"publisher","first-page":"3948","DOI":"10.1021\/acs.jmedchem.4c01257","article-title":"Artificial intelligence in natural product drug discovery: current applications and future perspectives","volume":"68","author":"Gangwal","year":"2025","journal-title":"J Med Chem"},{"key":"2025122308564817300_ref108","doi-asserted-by":"crossref","DOI":"10.3390\/pharmaceutics14040832","article-title":"An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors","volume":"14","author":"Jaganathan","year":"2022","journal-title":"Pharmaceutics"},{"key":"2025122308564817300_ref109","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.48550\/arXiv.1602.04938","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Ribeiro","year":"2016"},{"key":"2025122308564817300_ref110","doi-asserted-by":"publisher","article-title":"Dropout as a Bayesian approximation: representing model uncertainty in deep learning","author":"Gal","DOI":"10.48550\/arXiv.1506.02142"},{"key":"2025122308564817300_ref111","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Information Fusion"},{"key":"2025122308564817300_ref112","doi-asserted-by":"publisher","first-page":"2855","DOI":"10.1021\/acs.molpharmaceut.6b00471","article-title":"ADMET evaluation in drug discovery. 16. Predicting hERG blockers by combining multiple pharmacophores and machine learning approaches","volume":"13","author":"Wang","year":"2016","journal-title":"Mol Pharm"},{"key":"2025122308564817300_ref113","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1021\/acs.jcim.8b00769","article-title":"Deep learning-based prediction of drug-induced cardiotoxicity","volume":"59","author":"Cai","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref114","doi-asserted-by":"publisher","first-page":"3049","DOI":"10.1093\/bioinformatics\/btaa075","article-title":"DeepHIT: A deep learning framework for prediction of hERG-induced cardiotoxicity","volume":"36","author":"Ryu","year":"2020","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref115","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1021\/acs.jcim.3c01301","article-title":"Benchmarking of small molecule feature representations for hERG, Nav1.5, and Cav1.2 cardiotoxicity prediction","volume":"64","author":"Arab","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref116","first-page":"30","article-title":"CardioGenAI: A machine learning-based framework for re-engineering drugs for reduced hERG liability","volume":"17","author":"Kyro","year":"2025","journal-title":"J Chem"},{"key":"2025122308564817300_ref117","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1021\/acs.jcim.4c02079","article-title":"StackDILI: enhancing drug-induced liver injury prediction through stacking strategy with effective molecular representations","volume":"65","author":"Guan","year":"2025","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref118","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2024\/8897847","article-title":"InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism","volume":"16","author":"Lee","year":"2024","journal-title":"J Chem"},{"key":"2025122308564817300_ref119","doi-asserted-by":"publisher","first-page":"13502","DOI":"10.1021\/acsomega.5c00075","article-title":"pDILI_v1: A web-based machine learning tool for predicting drug-induced liver injury (DILI) integrating chemical space analysis and molecular fingerprints","volume":"10","author":"Amin","year":"2025","journal-title":"ACS Omega"},{"key":"2025122308564817300_ref120","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1021\/acs.chemrestox.4c00015","article-title":"Improved detection of drug-induced liver injury by integrating predicted In vivo and In vitro data","volume":"37","author":"Seal","year":"2024","journal-title":"Chem Res Toxicol"},{"key":"2025122308564817300_ref121","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1097\/HEP.0000000000000628","article-title":"An entropy weight method to integrate big omics and mechanistically evaluate DILI","volume":"79","author":"Jin","year":"2024","journal-title":"Hepatology"},{"key":"2025122308564817300_ref122","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1093\/toxsci\/kfab157","article-title":"Tox-GAN: an artificial intelligence approach alternative to animal studies\u2014A case study with Toxicogenomics","volume":"186","author":"Chen","year":"2022","journal-title":"Toxicol Sci"},{"key":"2025122308564817300_ref123","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2021.793332","article-title":"In Silico prediction and insights into the structural basis of drug induced nephrotoxicity","volume":"12","author":"Shi","year":"2022","journal-title":"Front Pharmacol"},{"key":"2025122308564817300_ref124","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1002\/jat.4331","article-title":"In silico prediction of potential drug-induced nephrotoxicity with machine learning methods","volume":"42","author":"Gong","year":"2022","journal-title":"J Appl Toxicol"},{"key":"2025122308564817300_ref125","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1007\/s42979-023-02258-2","article-title":"Predicting renal toxicity of compounds with deep learning and machine learning methods","volume":"4","author":"Mazumdar","year":"2023","journal-title":"SN Computer Science"},{"key":"2025122308564817300_ref126","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/TENCON61640.2024.10902993","volume-title":"TENCON 2024\u20132024 IEEE Region 10 Conference (TENCON)","author":"Nguyen-Vo","year":"2024"},{"key":"2025122308564817300_ref127","doi-asserted-by":"publisher","first-page":"e1010402","DOI":"10.1371\/journal.pcbi.1010402","article-title":"A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data","volume":"18","author":"Su","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"2025122308564817300_ref128","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s12859-023-05176-5","article-title":"PredAOT: A computational framework for prediction of acute oral toxicity based on multiple random forest models","volume":"24","author":"Ryu","year":"2023","journal-title":"BMC Bioinformatics"},{"key":"2025122308564817300_ref129","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1093\/toxsci\/kfad025","article-title":"Profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning","volume":"193","author":"Wijeyesakere","year":"2023","journal-title":"Toxicol Sci"},{"key":"2025122308564817300_ref130","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1021\/acs.chemrestox.4c00012","article-title":"In Silico prediction of chemical acute dermal toxicity using explainable machine learning methods","volume":"37","author":"Lou","year":"2024","journal-title":"Chem Res Toxicol"},{"key":"2025122308564817300_ref131","doi-asserted-by":"crossref","first-page":"27012","DOI":"10.1289\/EHP9341","article-title":"STopTox: an in Silico alternative to animal testing for acute systemic and topical toxicity","volume":"130","author":"Borba","year":"2022","journal-title":"Environ Health Perspect"},{"key":"2025122308564817300_ref132","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1021\/acs.jcim.0c01164","article-title":"Large-scale Modeling of multispecies acute toxicity end points using consensus of multitask deep learning methods","volume":"61","author":"Jain","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref133","doi-asserted-by":"publisher","first-page":"110921","DOI":"10.1016\/j.fct.2019.110921","article-title":"CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens","volume":"135","author":"Wang","year":"2020","journal-title":"Food Chem Toxicol"},{"key":"2025122308564817300_ref134","doi-asserted-by":"publisher","first-page":"i84","DOI":"10.1093\/bioinformatics\/btac266","article-title":"A graph neural network approach for molecule carcinogenicity prediction","volume":"38","author":"Fradkin","year":"2022","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref135","doi-asserted-by":"crossref","DOI":"10.3390\/s22218185","article-title":"Predicting chemical carcinogens using a hybrid neural network deep learning method","volume":"22","author":"Limbu","year":"2022","journal-title":"Sensors (Basel)"},{"key":"2025122308564817300_ref136","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1111\/jcmm.17889","article-title":"DCAMCP: A deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction","volume":"27","author":"Chen","year":"2023","journal-title":"J Cell Mol Med"},{"key":"2025122308564817300_ref137","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1038\/s41589-022-01110-7","article-title":"Artificial intelligence uncovers carcinogenic human metabolites","volume":"18","author":"Mittal","year":"2022","journal-title":"Nat Chem Biol"},{"key":"2025122308564817300_ref138","doi-asserted-by":"publisher","DOI":"10.3389\/fenvs.2015.00085","article-title":"Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs, Frontiers in environmental","volume":"3","author":"R","year":"2016","journal-title":"Science"},{"key":"2025122308564817300_ref139","doi-asserted-by":"publisher","DOI":"10.3389\/fenvs.2015.00080","article-title":"DeepTox: toxicity prediction using deep learning, Frontiers in environmental","volume":"3","author":"A","year":"2016","journal-title":"Science"},{"key":"2025122308564817300_ref140","volume-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019","author":"Jiang"},{"key":"2025122308564817300_ref141","author":"Baek"},{"key":"2025122308564817300_ref142","doi-asserted-by":"crossref","first-page":"2559","DOI":"10.1145\/3442381.3450112","volume-title":"Proceedings of the Web Conference 2021. Ljubljana, Slovenia: Association for Computing Machinery","author":"Guo","year":"2021"},{"key":"2025122308564817300_ref143","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1109\/JBHI.2024.3510297","article-title":"CardiOT: towards interpretable drug cardiotoxicity prediction using optimal transport and Kolmogorov--Arnold networks","volume":"29","author":"Zhang","year":"2025","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025122308564817300_ref144","doi-asserted-by":"publisher","first-page":"8185","DOI":"10.3390\/s22218185","article-title":"Predicting chemical carcinogens using a hybrid neural network deep learning method","volume":"22","author":"Limbu","year":"2022","journal-title":"Sensors"},{"key":"2025122308564817300_ref145","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1016\/j.phymed.2018.01.018","article-title":"A strategy for the discovery and validation of toxicity quality marker of Chinese medicine based on network toxicology","volume":"54","author":"Li","year":"2019","journal-title":"Phytomedicine"},{"key":"2025122308564817300_ref146","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1002\/imt2.42","article-title":"TCM2COVID: A resource of anti-COVID-19 traditional Chinese medicine with effects and mechanisms","volume":"1","author":"Ren","year":"2022","journal-title":"iMeta"},{"key":"2025122308564817300_ref147","doi-asserted-by":"publisher","first-page":"116563","DOI":"10.1016\/j.ecoenv.2024.116563","article-title":"Maintaining calcium homeostasis as a strategy to alleviate nephrotoxicity caused by evodiamine","volume":"281","author":"Yang","year":"2024","journal-title":"Ecotoxicol Environ Saf"},{"key":"2025122308564817300_ref148","doi-asserted-by":"publisher","DOI":"10.2174\/0113894501329810241117231839","article-title":"Pharmacological and therapeutic potential of a natural flavonoid Icariside II in human complication","volume":"26","author":"Singh","year":"2025","journal-title":"Curr Drug Targets"},{"key":"2025122308564817300_ref149","doi-asserted-by":"publisher","first-page":"530","DOI":"10.2174\/0113894501281496231226070459","article-title":"Effect of rotenone on the neurodegeneration among different models","volume":"25","author":"Subhan","year":"2024","journal-title":"Curr Drug Targets"},{"key":"2025122308564817300_ref150","doi-asserted-by":"publisher","first-page":"2920","DOI":"10.4268\/cjcmm20112104","article-title":"Network toxicology and its application to traditional Chinese medicine","volume":"36","author":"Fan","year":"2011","journal-title":"Zhongguo Zhong Yao Za Zhi"},{"key":"2025122308564817300_ref151","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/S1875-5364(13)60037-0","article-title":"Traditional Chinese medicine network pharmacology: theory, methodology and application","volume":"11","author":"Li","year":"2013","journal-title":"Chin J Nat Med"},{"key":"2025122308564817300_ref152","first-page":"13","article-title":"TCMSP: A database of systems pharmacology for drug discovery from herbal medicines","volume":"6","author":"Ru","year":"2014","journal-title":"J Chem"},{"key":"2025122308564817300_ref153","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1191\/0960327102ht233oa","article-title":"Human carcinogens: an evaluation study via the COMPACT and HazardExpert procedures","volume":"21","author":"Lewi","year":"2002","journal-title":"Hum Exp Toxicol"},{"key":"2025122308564817300_ref154","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1002\/1098-2280(2001)37:1&lt;55::AID-EM1006&gt;3.0.CO;2-5","article-title":"Evaluation of the TOPKAT system for predicting the carcinogenicity of chemicals","volume":"37","author":"Prival","year":"2001","journal-title":"Environ Mol Mutagen"},{"key":"2025122308564817300_ref155","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/10629369908039182","article-title":"Knowledge-based expert systems for toxicity and metabolism prediction: DEREK","volume":"10","author":"Greene","year":"1999","journal-title":"StAR and METEOR, SAR QSAR Environ Res"},{"key":"2025122308564817300_ref156","first-page":"39","article-title":"Update: A graph theory library for visualization and analysis","volume":"2023","author":"Franz","year":"2023","journal-title":"Bioinformatics"},{"key":"2025122308564817300_ref157","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1038\/nprot.2016.051","article-title":"Computational protein-ligand docking and virtual drug screening with the AutoDock suite","volume":"11","author":"Forli","year":"2016","journal-title":"Nat Protoc"},{"key":"2025122308564817300_ref158","doi-asserted-by":"publisher","first-page":"D1089","DOI":"10.1093\/nar\/gks1100","article-title":"TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis","volume":"41","author":"Xue","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref159","doi-asserted-by":"publisher","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref160","doi-asserted-by":"crossref","DOI":"10.3390\/toxins14070486","article-title":"Study on the mechanism of Mesaconitine-induced hepatotoxicity in rats based on Metabonomics and toxicology network","volume":"14","author":"Chen","year":"2022","journal-title":"Toxins (Basel)"},{"key":"2025122308564817300_ref161","doi-asserted-by":"publisher","first-page":"102075","DOI":"10.1016\/j.omtn.2023.102075","article-title":"Unveiling the mechanisms of nephrotoxicity caused by nephrotoxic compounds using toxicological network analysis","volume":"34","author":"Xi","year":"2023","journal-title":"Mol Ther Nucleic Acids"},{"key":"2025122308564817300_ref162","doi-asserted-by":"publisher","first-page":"106899","DOI":"10.1016\/j.compbiomed.2023.106899","article-title":"Assessment of palmitic acid toxicity to animal hearts and other major organs based on acute toxicity, network pharmacology, and molecular docking","volume":"158","author":"Lv","year":"2023","journal-title":"Comput Biol Med"},{"key":"2025122308564817300_ref163","doi-asserted-by":"publisher","first-page":"125731","DOI":"10.1109\/ACCESS.2020.3006097","article-title":"Artificial intelligence image recognition method based on convolutional neural network algorithm","volume":"8","author":"Tian","year":"2020","journal-title":"IEEE Access"},{"key":"2025122308564817300_ref164","doi-asserted-by":"publisher","first-page":"119480","DOI":"10.1016\/j.jep.2025.119480","article-title":"Application and development trends of network toxicology in the safety assessment of traditional Chinese medicine","volume":"343","author":"Li","year":"2025","journal-title":"J Ethnopharmacol"},{"key":"2025122308564817300_ref165","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac434","article-title":"MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms","volume":"23","author":"Tian","year":"2022","journal-title":"Brief Bioinform"},{"key":"2025122308564817300_ref166","doi-asserted-by":"publisher","first-page":"e15939","DOI":"10.1371\/journal.pone.0015939","article-title":"TCM database@Taiwan: the world's largest traditional Chinese medicine database for drug screening in silico","volume":"6","author":"Chen","year":"2011","journal-title":"PLoS One"},{"key":"2025122308564817300_ref167","doi-asserted-by":"crossref","first-page":"2821","DOI":"10.1038\/s41598-017-03039-7","article-title":"TCM-mesh: the database and analytical system for network pharmacology analysis for TCM preparations","volume":"7","author":"Zhang","year":"2017","journal-title":"Sci Rep"},{"key":"2025122308564817300_ref168","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/1472-6882-8-58","article-title":"TCMGeneDIT: A database for associated traditional Chinese medicine, gene and disease information using text mining","volume":"8","author":"Fang","year":"2008","journal-title":"BMC Complement Altern Med"},{"key":"2025122308564817300_ref169","first-page":"28","article-title":"HIM-herbal ingredients in-vivo metabolism database","volume":"5","author":"Kang","year":"2013","journal-title":"J Chem"},{"key":"2025122308564817300_ref170","doi-asserted-by":"publisher","first-page":"2189","DOI":"10.1007\/s11427-022-2318-4","article-title":"TCMSTD 1.0: A systematic analysis of the traditional Chinese medicine system toxicology database","volume":"66","author":"Song","year":"2023","journal-title":"Sci China Life Sci"},{"key":"2025122308564817300_ref171","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s41392-023-01339-1","article-title":"TCMBank-the largest TCM database provides deep learning-based Chinese-Western medicine exclusion prediction","volume":"8","author":"Lv","year":"2023","journal-title":"Signal Transduct Target Ther"},{"key":"2025122308564817300_ref172","doi-asserted-by":"publisher","first-page":"4948","DOI":"10.1021\/acs.jcim.3c00365","article-title":"DCABM-TCM: A database of constituents absorbed into the blood and metabolites of traditional Chinese medicine","volume":"63","author":"Liu","year":"2023","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref173","doi-asserted-by":"publisher","first-page":"D1110","DOI":"10.1093\/nar\/gkad926","article-title":"BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins","volume":"52","author":"Kong","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"2025122308564817300_ref174","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1186\/s12915-024-02028-3","article-title":"DrugReAlign: A multisource prompt framework for drug repurposing based on large language models","volume":"22","author":"Wei","year":"2024","journal-title":"BMC Biol"},{"key":"2025122308564817300_ref175","doi-asserted-by":"crossref","first-page":"5528","DOI":"10.1038\/s41467-025-60577-9","article-title":"Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model","volume":"16","author":"Wang","year":"2025","journal-title":"Nat Commun"},{"key":"2025122308564817300_ref176","doi-asserted-by":"publisher","first-page":"4382","DOI":"10.1097\/JS9.0000000000000719","article-title":"ChatGPT or LLM in next-generation drug discovery and development: pharmaceutical and biotechnology companies can make use of the artificial intelligence-based device for a faster way of drug discovery and development","volume":"109","author":"Pal","year":"2023","journal-title":"Int J Surg"},{"key":"2025122308564817300_ref177","doi-asserted-by":"publisher","first-page":"e2440969","DOI":"10.1001\/jamanetworkopen.2024.40969","article-title":"Large language model influence on diagnostic reasoning: A randomized clinical trial","volume":"7","author":"Goh","year":"2024","journal-title":"JAMA Netw Open"},{"key":"2025122308564817300_ref178","first-page":"2024.2006.2021.24309315","article-title":"UniTox: leveraging LLMs to curate a unified dataset of drug-induced Toxicity from FDA labels","author":"Silberg","year":"2024","journal-title":"medRxiv"},{"key":"2025122308564817300_ref179","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1038\/s41597-024-03793-0","article-title":"PharmaBench: enhancing ADMET benchmarks with large language models","volume":"11","author":"Niu","year":"2024","journal-title":"Scientific Data"},{"key":"2025122308564817300_ref180","doi-asserted-by":"publisher","first-page":"2268","DOI":"10.1021\/acs.jcim.4c01371","article-title":"Large language models as tools for molecular toxicity prediction: AI insights into cardiotoxicity","volume":"65","author":"Yang","year":"2025","journal-title":"J Chem Inf Model"},{"key":"2025122308564817300_ref181","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1038\/s41591-024-03445-1","article-title":"Medical large language models are vulnerable to data-poisoning attacks","volume":"31","author":"Alber","year":"2025","journal-title":"Nat Med"},{"key":"2025122308564817300_ref182","doi-asserted-by":"publisher","author":"Hakim","DOI":"10.48550\/arXiv.2407.18322"},{"key":"2025122308564817300_ref183","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13000-024-01464-7","article-title":"Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology\u2014a recent scoping review","volume":"19","author":"Ullah","year":"2024","journal-title":"Diagn Pathol"},{"key":"2025122308564817300_ref184","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1177\/01655515221112844","article-title":"Knowledge-graph-based explainable AI: A systematic review","volume":"50","author":"Rajabi","year":"2024","journal-title":"J Inf Sci"},{"key":"2025122308564817300_ref185","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2025.103268","volume-title":"Artif Intell Med","author":"Wang","year":"2025"},{"key":"2025122308564817300_ref186","doi-asserted-by":"publisher","DOI":"10.2174\/0113894501347158250305074908","article-title":"The role of glycolipids and their toxicity in the context of nanomaterials and nanoparticles: A review of the literature","volume":"26","author":"Li","journal-title":"Curr Drug Targets"},{"key":"2025122308564817300_ref187","doi-asserted-by":"publisher","first-page":"17690","DOI":"10.1021\/acs.est.3c00653","article-title":"Advancing computational toxicology by interpretable machine learning","volume":"57","author":"Jia","year":"2023","journal-title":"Environ Sci Technol"},{"key":"2025122308564817300_ref188","doi-asserted-by":"publisher","first-page":"4130","DOI":"10.1021\/acssuschemeng.0c09196","article-title":"Integration of computational toxicology, Toxicogenomics data mining, and omics techniques to unveil toxicity pathways","volume":"9","author":"Wang","year":"2021","journal-title":"ACS Sustain Chem Eng"},{"key":"2025122308564817300_ref189","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1093\/ilar\/ilw031","article-title":"Refinement, reduction, and replacement of animal toxicity tests by computational methods","volume":"57","author":"Ford","year":"2017","journal-title":"ILAR J"},{"key":"2025122308564817300_ref190","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1021\/acs.chemrestox.1c00032","article-title":"Introduction to special issue: computational toxicology","volume":"34","author":"Kleinstreuer","year":"2021","journal-title":"Chem Res Toxicol"},{"key":"2025122308564817300_ref191","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13020-024-01056-z","article-title":"Network pharmacology: A crucial approach in traditional Chinese medicine research","volume":"20","author":"Zhai","year":"2025","journal-title":"Chin Med"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/5\/bbaf533\/64521542\/bbaf533.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/5\/bbaf533\/64521542\/bbaf533.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T13:57:01Z","timestamp":1766498221000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf533\/8276062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,31]]},"references-count":191,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,8,31]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf533","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,9]]},"published":{"date-parts":[[2025,8,31]]},"article-number":"bbaf533"}}