{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T20:19:21Z","timestamp":1772655561404,"version":"3.50.1"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:00:00Z","timestamp":1767312000000},"content-version":"vor","delay-in-days":62,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Guangdong Young Scholar Development Fund of Shenzhen Ganghong Group Co., Ltd.","award":["2021E0005"],"award-info":[{"award-number":["2021E0005"]}]},{"name":"Guangdong Young Scholar Development Fund of Shenzhen Ganghong Group Co., Ltd.","award":["2022E0035"],"award-info":[{"award-number":["2022E0035"]}]},{"name":"Guangdong Young Scholar Development Fund of Shenzhen Ganghong Group Co., Ltd.","award":["2023E0012"],"award-info":[{"award-number":["2023E0012"]}]},{"name":"Shenzhen-Hong Kong Cooperation Zone for Technology and Innovation","award":["HZQB-KCZYB-2020056"],"award-info":[{"award-number":["HZQB-KCZYB-2020056"]}]},{"name":"Shenzhen-Hong Kong Cooperation Zone for Technology and Innovation","award":["P2\u20132022-HDH-001-A"],"award-info":[{"award-number":["P2\u20132022-HDH-001-A"]}]},{"name":"Warshel Institute for Computational Biology funding from Shenzhen City and Longgang District","award":["LGKCSDPT2025001"],"award-info":[{"award-number":["LGKCSDPT2025001"]}]},{"name":"Guangdong S&T programme","award":["2024A0505050001"],"award-info":[{"award-number":["2024A0505050001"]}]},{"name":"Guangdong S&T programme","award":["2024A0505050002"],"award-info":[{"award-number":["2024A0505050002"]}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["JCYJ20220530143615035"],"award-info":[{"award-number":["JCYJ20220530143615035"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Adverse drug reactions (ADRs) are a major cause of clinical trial failure and postmarket withdrawal, posing significant risks to public health and impeding drug development. While computational methods offer an alternative to costly preclinical testing, existing models often fail with novel compounds by requiring pre-existing information such as drug-ADR associations or by inadequately integrating diverse data sources. Here, we introduce DeepADR, a multimodal deep learning framework for predicting both the occurrence and frequency of ADRs using early-stage, readily available data. DeepADR integrates chemical structures and biological target profiles with semantic representations of ADR terms derived from a large language model (LLMs). These heterogeneous parameters are fused using a Kolmogorov\u2013Arnold Network (KAN), which enhances the modeling of complex, nonlinear relationships among modalities to improve predictive performance. Our model outperforms existing methods in predicting both ADR occurrence and frequency, demonstrating robust generalization to new chemical entities. DeepADR showed consistently better performance than other models across both classification and regression tasks. By effectively integrating chemical, biological, and semantic datasets, DeepADR provides a powerful, scalable tool for the early-stage safety assessment and candidate prioritization. This framework not only facilitates the prioritization of safer drug candidates but also offers a methodology for predicting the toxicity of other hazardous materials, holding significant promise for advancing public health.<\/jats:p>","DOI":"10.1093\/bib\/bbaf695","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T12:57:19Z","timestamp":1766062639000},"source":"Crossref","is-referenced-by-count":1,"title":["DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov\u2013Arnold networks"],"prefix":"10.1093","volume":"26","author":[{"given":"Jingting","family":"Wan","sequence":"first","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyang","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danhong","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yigang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang-Chi-Dung","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yisheng","family":"He","sequence":"additional","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8453-4939","authenticated-orcid":false,"given":"Hsi-Yuan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2857-7023","authenticated-orcid":false,"given":"Hsien-Da","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Guangdong Provincial Key Laboratory of Digital Biology and Drug Development, The Chinese University of Hong Kong , Shenzhen, No. 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172 ,","place":["P.R. China"]},{"name":"Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College , No. 157 Malianwa North Road, Haidian District, Beijing 100730 ,","place":["P.R. China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"2026010207403184100_ref1","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1016\/S0140-6736(00)02799-9","article-title":"Adverse drug reactions: definitions, diagnosis, and management","volume":"356","author":"Edwards","year":"2000","journal-title":"Lancet"},{"key":"2026010207403184100_ref2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1001\/jama.1995.03530010043033","article-title":"Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE prevention study group","volume":"274","author":"Bates","year":"1995","journal-title":"JAMA"},{"key":"2026010207403184100_ref3","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1001\/jama.279.15.1200","article-title":"Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies","volume":"279","author":"Lazarou","year":"1998","journal-title":"JAMA"},{"key":"2026010207403184100_ref4","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1109\/tcbb.2018.2850884","article-title":"Drug side-effect profiles prediction: from empirical to structural risk minimization","volume":"17","author":"Jiang","year":"2020","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2026010207403184100_ref5","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1093\/bib\/bbv020","article-title":"A survey of current trends in computational drug repositioning","volume":"17","author":"Li","year":"2016","journal-title":"Brief Bioinform"},{"key":"2026010207403184100_ref6","doi-asserted-by":"publisher","first-page":"1421","DOI":"10.1016\/S1359-6446(05)03632-9","article-title":"Keynote review: in vitro safety pharmacology profiling: an essential tool for successful drug development","volume":"10","author":"Whitebread","year":"2005","journal-title":"Drug Discov Today"},{"key":"2026010207403184100_ref7","doi-asserted-by":"publisher","first-page":"195902","DOI":"10.1007\/s11704-024-31063-0","article-title":"Application of machine learning in drug side effect prediction: databases, methods, and challenges","volume":"19","author":"Zhao","year":"2025","journal-title":"Front Comp Sci"},{"key":"2026010207403184100_ref8","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1186\/1471-2105-12-169","article-title":"Predicting drug side-effect profiles: a chemical fragment-based approach","volume":"12","author":"Pauwels","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2026010207403184100_ref9","doi-asserted-by":"publisher","first-page":"e28","DOI":"10.1136\/amiajnl-2011-000699","article-title":"Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs","volume":"19","author":"Liu","year":"2012","journal-title":"J Am Med Inform Assoc"},{"key":"2026010207403184100_ref10","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1038\/s41598-017-00908-z","article-title":"Predicting neurological adverse drug reactions based on biological, chemical and phenotypic properties of drugs using machine learning models","volume":"7","author":"Jamal","year":"2017","journal-title":"Sci Rep"},{"key":"2026010207403184100_ref11","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1186\/s12859-015-0774-y","article-title":"Predicting drug side effects by multi-label learning and ensemble learning","volume":"16","author":"Zhang","year":"2015","journal-title":"BMC Bioinformatics"},{"key":"2026010207403184100_ref12","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.neucom.2018.01.085","article-title":"Feature-derived graph regularized matrix factorization for predicting drug side effects","volume":"287","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"2026010207403184100_ref13","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1186\/1471-2105-14-207","article-title":"Integrative relational machine-learning for understanding drug side-effect profiles","volume":"14","author":"Bresso","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2026010207403184100_ref14","doi-asserted-by":"publisher","first-page":"2338","DOI":"10.1093\/bioinformatics\/btw168","article-title":"Drug-induced adverse events prediction with the LINCS L1000 data","volume":"32","author":"Wang","year":"2016","journal-title":"Bioinformatics"},{"key":"2026010207403184100_ref15","doi-asserted-by":"publisher","first-page":"979","DOI":"10.1016\/j.neucom.2015.08.054","article-title":"Predicting potential side effects of drugs by recommender methods and ensemble learning","volume":"173","author":"Zhang","year":"2016","journal-title":"Neurocomputing"},{"key":"2026010207403184100_ref16","doi-asserted-by":"publisher","first-page":"481","DOI":"10.32604\/cmc.2019.05536","article-title":"Drug side-effect prediction using heterogeneous features and bipartite local models","volume":"60","author":"Zheng","year":"2019","journal-title":"Comput Mater Contin"},{"key":"2026010207403184100_ref17","doi-asserted-by":"publisher","first-page":"bbac458","DOI":"10.1093\/bib\/bbac458","article-title":"Identification of drug-side effect association via restricted Boltzmann machines with penalized term","volume":"23","author":"Qian","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026010207403184100_ref18","doi-asserted-by":"publisher","first-page":"107812","DOI":"10.1016\/j.compbiomed.2023.107812","article-title":"MSDSE: predicting drug-side effects based on multi-scale features and deep multi-structure neural network","volume":"169","author":"Yu","year":"2024","journal-title":"Comput Biol Med"},{"key":"2026010207403184100_ref19","doi-asserted-by":"publisher","first-page":"16216","DOI":"10.3390\/ijms232416216","article-title":"iADRGSE: a graph-embedding and self-attention encoding for identifying adverse drug reaction in the earlier phase of drug development","volume":"23","author":"Cheng","year":"2022","journal-title":"Int J Mol Sci"},{"key":"2026010207403184100_ref20","doi-asserted-by":"publisher","first-page":"5124","DOI":"10.1021\/acs.jcim.5c00136","article-title":"Multi-knowledge graph and multi-view entity feature learning for predicting drug-related side effects","volume":"65","author":"Xuan","year":"2025","journal-title":"J Chem Inf Model"},{"key":"2026010207403184100_ref21","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1126\/science.1158140","article-title":"Drug target identification using side-effect similarity","volume":"321","author":"Campillos","year":"2008","journal-title":"Science"},{"key":"2026010207403184100_ref22","doi-asserted-by":"publisher","first-page":"bbac126","DOI":"10.1093\/bib\/bbac126","article-title":"Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction","volume":"23","author":"Xuan","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026010207403184100_ref23","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12918-017-0477-2","article-title":"A unified frame of predicting side effects of drugs by using linear neighborhood similarity","volume":"11","author":"Zhang","year":"2017","journal-title":"BMC Syst Biol"},{"key":"2026010207403184100_ref24","doi-asserted-by":"publisher","first-page":"S18","DOI":"10.1186\/1752-0509-7-s6-s18","article-title":"Inferring protein domains associated with drug side effects based on drug-target interaction network","volume":"7 Suppl 6","author":"Iwata","year":"2013","journal-title":"BMC Syst Biol"},{"key":"2026010207403184100_ref25","doi-asserted-by":"publisher","first-page":"102357","DOI":"10.1016\/j.ipm.2020.102357","article-title":"NDDSA: a network- and domain-based method for predicting drug-side effect associations","volume":"57","author":"Shabani-Mashcool","year":"2020","journal-title":"Inf Process Manag"},{"key":"2026010207403184100_ref26","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1186\/s12859-018-2563-x","article-title":"Inverse similarity and reliable negative samples for drug side-effect prediction","volume":"19","author":"Zheng","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2026010207403184100_ref27","doi-asserted-by":"publisher","first-page":"4575","DOI":"10.1038\/s41467-020-18305-y","article-title":"Predicting the frequencies of drug side effects","volume":"11","author":"Galeano","year":"2020","journal-title":"Nat Commun"},{"key":"2026010207403184100_ref28","doi-asserted-by":"publisher","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2026010207403184100_ref29","doi-asserted-by":"publisher","first-page":"btad532","DOI":"10.1093\/bioinformatics\/btad532","article-title":"A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug\u2013side effects","volume":"39","author":"Wang","year":"2023","journal-title":"Bioinformatics"},{"key":"2026010207403184100_ref30","volume-title":"Medical Dictionary for Regulatory Activities (MedDRA), Version 28.0","author":"International Council for Harmonisation (ICH)","year":"2025"},{"key":"2026010207403184100_ref31","doi-asserted-by":"publisher","first-page":"bbab239","DOI":"10.1093\/bib\/bbab239","article-title":"A novel graph attention model for predicting frequencies of drug-side effects from multi-view data","volume":"22","author":"Zhao","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026010207403184100_ref32","doi-asserted-by":"publisher","first-page":"bbab449","DOI":"10.1093\/bib\/bbab449","article-title":"A similarity-based deep learning approach for determining the frequencies of drug side effects","volume":"23","author":"Zhao","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026010207403184100_ref33","doi-asserted-by":"publisher","first-page":"bbab586","DOI":"10.1093\/bib\/bbab586","article-title":"DSGAT: predicting frequencies of drug side effects by graph attention networks","volume":"23","author":"Xu","year":"2022","journal-title":"Brief Bioinform"},{"key":"2026010207403184100_ref34","doi-asserted-by":"publisher","first-page":"104098","DOI":"10.1016\/j.jbi.2022.104098","article-title":"Idse-HE: hybrid embedding graph neural network for drug side effects prediction","volume":"131","author":"Yu","year":"2022","journal-title":"J Biomed Inform"},{"key":"2026010207403184100_ref35","doi-asserted-by":"publisher","first-page":"e2404671","DOI":"10.1002\/advs.202404671","article-title":"Precision adverse drug reactions prediction with heterogeneous graph neural network","volume":"12","author":"Gao","year":"2024","journal-title":"Adv Sci (Weinh)"},{"key":"2026010207403184100_ref36","doi-asserted-by":"publisher","first-page":"109282","DOI":"10.1016\/j.compbiomed.2024.109282","article-title":"Graph neural network-based subgraph analysis for predicting adverse drug events","volume":"183","author":"Zhou","year":"2024","journal-title":"Comput Biol Med"},{"key":"2026010207403184100_ref37","doi-asserted-by":"publisher","first-page":"106779","DOI":"10.1016\/j.neunet.2024.106779","article-title":"HSTrans: homogeneous substructures transformer for predicting frequencies of drug-side effects","volume":"181","author":"Xu","year":"2025","journal-title":"Neural Netw"},{"key":"2026010207403184100_ref38","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1186\/s12859-024-05915-2","article-title":"Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency","volume":"25","author":"Baek","year":"2024","journal-title":"BMC Bioinformatics"},{"key":"2026010207403184100_ref39","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/nrd3078","article-title":"How to improve R&D productivity: the pharmaceutical industry\u2019s grand challenge","volume":"9","author":"Paul","year":"2010","journal-title":"Nat Rev Drug Discov"},{"key":"2026010207403184100_ref40","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1038\/s42256-022-00580-7","article-title":"Large-scale chemical language representations capture molecular structure and properties","volume":"4","author":"Ross","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2026010207403184100_ref41"},{"key":"2026010207403184100_ref42","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","article-title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","volume":"36","author":"Lee","year":"2020","journal-title":"Bioinformatics"},{"key":"2026010207403184100_ref43","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2026010207403184100_ref44","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc IEEE"},{"key":"2026010207403184100_ref45"},{"key":"2026010207403184100_ref46","doi-asserted-by":"publisher","first-page":"D1373","DOI":"10.1093\/nar\/gkac956","article-title":"PubChem 2023 update","volume":"51","author":"Kim","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2026010207403184100_ref47","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":"2026010207403184100_ref48"},{"key":"2026010207403184100_ref49","doi-asserted-by":"publisher","first-page":"CD001911","DOI":"10.1002\/14651858.CD001911.pub4","article-title":"Carbamazepine versus phenytoin monotherapy for epilepsy: an individual participant data review","volume":"7","author":"Nevitt","year":"2019","journal-title":"Cochrane Database Syst Rev"},{"key":"2026010207403184100_ref50","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1017\/s1461145704004651","article-title":"A review of the evidence for carbamazepine and oxcarbazepine in the treatment of bipolar disorder","volume":"7","author":"Hirschfeld","year":"2004","journal-title":"Int J Neuropsychopharmacol"},{"key":"2026010207403184100_ref51","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s11046-016-0045-0","article-title":"New antifungal agents and new formulations against dermatophytes","volume":"182","author":"Gupta","year":"2017","journal-title":"Mycopathologia"},{"key":"2026010207403184100_ref52","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1111\/j.1365-2133.1992.tb00001.x","article-title":"Terbinafine: mode of action and properties of the squalene epoxidase inhibition","volume":"126 Suppl 39","author":"Ryder","year":"1992","journal-title":"Br J Dermatol"},{"key":"2026010207403184100_ref53","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1164\/rccm.201111-2042ST","article-title":"An official American Thoracic Society statement: update on the mechanisms, assessment, and management of dyspnea","volume":"185","author":"Parshall","year":"2012","journal-title":"Am J Respir Crit Care Med"},{"key":"2026010207403184100_ref54","doi-asserted-by":"publisher","first-page":"86","DOI":"10.3109\/15622975.2015.1132007","article-title":"Assessment and management of agitation in psychiatry: expert consensus","volume":"17","author":"Garriga","year":"2016","journal-title":"World J Biol Psychiatry"},{"key":"2026010207403184100_ref55","doi-asserted-by":"publisher","first-page":"eaag1166","DOI":"10.1126\/scitranslmed.aag1166","article-title":"The druggable genome and support for target identification and validation in drug development","volume":"9","author":"Finan","year":"2017","journal-title":"Sci Transl Med"},{"key":"2026010207403184100_ref56","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1038\/s41586-023-06388-8","article-title":"From target discovery to clinical drug development with human genetics","volume":"620","author":"Trajanoska","year":"2023","journal-title":"Nature"},{"key":"2026010207403184100_ref57"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf695\/66204217\/bbaf695.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/6\/bbaf695\/66204217\/bbaf695.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T12:40:41Z","timestamp":1767357641000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf695\/8408361"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":57,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf695","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,11]]},"published":{"date-parts":[[2025,11,1]]},"article-number":"bbaf695"}}