{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T19:44:46Z","timestamp":1786218286280,"version":"build-2736575974"},"reference-count":70,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Guizhou Provincial Basic Research Program","award":["MS(2025)375"],"award-info":[{"award-number":["MS(2025)375"]}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["232300421165"],"award-info":[{"award-number":["232300421165"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32471309"],"award-info":[{"award-number":["32471309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence accelerates anticancer peptides (ACPs) discovery. However, existing computational methods lack integration of identification with activity-based candidate prioritization. Here, we present DeepACPred, a three-stage pipeline encompassing ACP binary classification model, ACP multilabel classification model, and ACP IC50 prediction model, leveraging multimodal features from ESM2 protein language model embeddings, AAindex physicochemical descriptors, and sequence composition. On 5712 benchmark sequences, the binary classifier achieved 95.10% accuracy (AUC\u2009=\u20090.9913), with performance remaining stable under CD-HIT cluster-aware splitting at 40%\u201390% identity thresholds. Multilabel cancer-type prediction yielded macro-F1\u2009=\u20090.9124 across seven cancer types, and log10(IC50) regression achieved Spearman \u03c1\u2009=\u20090.8602 under 5-fold cross-validation. Ablation experiments showed task-dependent feature contributions rather than uniformly additive multimodal effects. Applied to 260\u00a0000 motif-enriched 18-mer candidates, DeepACPred selected 12 peptides predicted to be active against breast cancer cells, all of which showed measurable in vitro cytotoxic activity against murine 4T1 cells in OD-derived dose\u2013response assays (IC50: 0.88\u201336.83\u00a0\u03bcg\/ml). Although prospective IC50 ranking showed limited fine-grained resolution, these results support the use of the regression module for coarse candidate enrichment. In conclusion, DeepACPred provides a systematic framework for ACP candidate enrichment and prioritization.<\/jats:p>","DOI":"10.1093\/bib\/bbag424","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T11:52:39Z","timestamp":1784116359000},"source":"Crossref","is-referenced-by-count":0,"title":["DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction"],"prefix":"10.1093","volume":"27","author":[{"given":"Bo","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Functional Molecules for Biomedicine of Zhengzhou City, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]},{"name":"College of Information Science and Engineering, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4824-0930","authenticated-orcid":false,"given":"Ruifang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Functional Molecules for Biomedicine of Zhengzhou City, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]},{"name":"College of Biological Engineering, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kedong","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]},{"name":"School of Pharmaceutical Sciences, Sun Yat-sen University , Guangzhou 511400 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Bioengineering, Zunyi Medical University , Zhuhai, Guangdong 519040 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Functional Molecules for Biomedicine of Zhengzhou City, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]},{"name":"College of Biological Engineering, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengwan","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Functional Molecules for Biomedicine of Zhengzhou City, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]},{"name":"College of Biological Engineering, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyu","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Functional Molecules for Biomedicine of Zhengzhou City, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]},{"name":"School of Artificial Intelligence and Big Data, Henan University of Technology , Zhengzhou, Henan 450001 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lujing","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Bioengineering, Zunyi Medical University , Zhuhai, Guangdong 519040 ,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"2026080815384801500_ref1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/s41586-019-1730-1","article-title":"A view on drug resistance in cancer","volume":"575","author":"Vasan","year":"2019","journal-title":"Nature"},{"key":"2026080815384801500_ref2","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/s41568-020-00302-4","article-title":"Non-genetic mechanisms of therapeutic resistance in cancer","volume":"20","author":"Marine","year":"2020","journal-title":"Nat Rev Cancer"},{"key":"2026080815384801500_ref3","doi-asserted-by":"publisher","first-page":"1310443","DOI":"10.3389\/fimmu.2024.1310443","article-title":"Bioactive peptides: an alternative therapeutic approach for cancer management","volume":"15","author":"Ghadiri","year":"2024","journal-title":"Front Immunol"},{"key":"2026080815384801500_ref4","doi-asserted-by":"publisher","first-page":"200004","DOI":"10.1098\/rsob.200004","article-title":"Anti-cancer peptides: classification, mechanism of action, reconstruction and modification","volume":"10","author":"Xie","year":"2020","journal-title":"Open Biol"},{"key":"2026080815384801500_ref5","doi-asserted-by":"publisher","first-page":"8109","DOI":"10.3390\/molecules27228109","article-title":"Antitumor activity and mechanism of action of the antimicrobial peptide AMP-17 on human leukemia K562 cells","volume":"27","author":"Tian","year":"2022","journal-title":"Molecules"},{"key":"2026080815384801500_ref6","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1016\/j.omtn.2020.05.006","article-title":"Deep-AmPEP30: improve short antimicrobial peptides prediction with deep learning","volume":"20","author":"Yan","year":"2020","journal-title":"Mol Ther Nucleic Acids"},{"key":"2026080815384801500_ref7","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/s41573-020-00135-8","article-title":"Trends in peptide drug discovery","volume":"20","author":"Muttenthaler","year":"2021","journal-title":"Nat Rev Drug Discov"},{"key":"2026080815384801500_ref8","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s40779-021-00343-2","article-title":"Antimicrobial peptides: mechanism of action, activity and clinical potential","volume":"8","author":"Zhang","year":"2021","journal-title":"Mil Med Res"},{"key":"2026080815384801500_ref9","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.3390\/ijms20081964","article-title":"mACPpred: a support vector machine-based meta-predictor for identification of anticancer peptides","volume":"20","author":"Boopathi","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2026080815384801500_ref10","doi-asserted-by":"publisher","first-page":"4007","DOI":"10.1093\/bioinformatics\/bty451","article-title":"ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides","volume":"34","author":"Wei","year":"2018","journal-title":"Bioinformatics"},{"key":"2026080815384801500_ref11","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.artmed.2017.06.008","article-title":"iACP-GAEnsC: evolutionary genetic algorithm-based ensemble classification of anticancer peptides by utilizing hybrid feature space","volume":"79","author":"Akbar","year":"2017","journal-title":"Artif Intell Med"},{"key":"2026080815384801500_ref12","doi-asserted-by":"publisher","first-page":"bbaa153","DOI":"10.1093\/bib\/bbaa153","article-title":"AntiCP 2.0: an updated model for predicting anticancer peptides","volume":"22","author":"Agrawal","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026080815384801500_ref13","doi-asserted-by":"publisher","first-page":"23676","DOI":"10.1038\/s41598-021-02703-3","article-title":"ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides","volume":"11","author":"Ahmed","year":"2021","journal-title":"Sci Rep"},{"key":"2026080815384801500_ref14","doi-asserted-by":"publisher","DOI":"10.1109\/IFUZZY63051.2024.10662887","article-title":"Enhancing protein sequence classification with a fuzzy neural network: a study in anticancer peptide identification","author":"Le","journal-title":"2024 International Conference on Fuzzy Theory and Its Applications (iFUZZY)"},{"key":"2026080815384801500_ref15","doi-asserted-by":"publisher","first-page":"bbac630","DOI":"10.1093\/bib\/bbac630","article-title":"Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding","volume":"24","author":"Yuan","year":"2023","journal-title":"Brief Bioinform"},{"key":"2026080815384801500_ref16","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.20365","article-title":"MLACP: machine-learning-based prediction of anticancer peptides","volume":"8","author":"Manavalan","year":"2017","journal-title":"Oncotarget"},{"key":"2026080815384801500_ref17","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.3390\/molecules24101973","article-title":"ACPred: a computational tool for the prediction and analysis of anticancer peptides","volume":"24","author":"Schaduangrat","year":"2019","journal-title":"Molecules"},{"key":"2026080815384801500_ref18","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/s12864-022-08310-4","article-title":"AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens","volume":"23","author":"Li","year":"2022","journal-title":"BMC Genomics"},{"key":"2026080815384801500_ref19","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1021\/acs.jcim.3c01860","article-title":"ACPScanner: prediction of anticancer peptides by integrated machine learning methodologies","volume":"64","author":"Zhong","year":"2024","journal-title":"J Chem Inf Model"},{"key":"2026080815384801500_ref20","doi-asserted-by":"publisher","first-page":"3789","DOI":"10.1021\/acs.jcim.1c00181","article-title":"xDeep-AcPEP: deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning","volume":"61","author":"Chen","year":"2021","journal-title":"J Chem Inf Model"},{"key":"2026080815384801500_ref21","doi-asserted-by":"publisher","first-page":"bbaa312","DOI":"10.1093\/bib\/bbaa312","article-title":"Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification","volume":"22","author":"Liang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2026080815384801500_ref22","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","article-title":"Evolutionary-scale prediction of atomic-level protein structure with a language model","volume":"379","author":"Lin","year":"2023","journal-title":"Science"},{"key":"2026080815384801500_ref23","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: toward understanding the language of life through self-supervised learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026080815384801500_ref24","doi-asserted-by":"publisher","first-page":"10427","DOI":"10.1021\/acs.jpclett.3c02398","article-title":"PeptideBERT: a language model based on transformers for peptide property prediction","volume":"14","author":"Guntuboina","year":"2023","journal-title":"J Phys Chem Lett"},{"key":"2026080815384801500_ref25","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.3390\/ijms18091856","article-title":"PSFM-DBT: identifying DNA-binding proteins by combing position specific frequency matrix and distance-bigram transformation","volume":"18","author":"Zhang","year":"2017","journal-title":"Int J Mol Sci"},{"key":"2026080815384801500_ref26","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1093\/bioinformatics\/bty140","article-title":"iFeature: a python package and web server for features extraction and selection from protein and peptide sequences","volume":"34","author":"Chen","year":"2018","journal-title":"Bioinformatics"},{"key":"2026080815384801500_ref27","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1016\/j.csbj.2021.03.022","article-title":"The language of proteins: NLP, machine learning & protein sequences","volume":"19","author":"Ofer","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"key":"2026080815384801500_ref28","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.cels.2021.05.017","article-title":"Learning the protein language: evolution, structure, and function","volume":"12","author":"Bepler","year":"2021","journal-title":"Cell Syst"},{"key":"2026080815384801500_ref29","doi-asserted-by":"publisher","first-page":"D202","DOI":"10.1093\/nar\/gkm998","article-title":"AAindex: amino acid index database, progress report 2008","volume":"36","author":"Kawashima","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2026080815384801500_ref30","doi-asserted-by":"publisher","first-page":"bbae460","DOI":"10.1093\/bib\/bbae460","article-title":"ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network","volume":"25","author":"Yao","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026080815384801500_ref31","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.omtn.2020.10.005","article-title":"DeepACP: a novel computational approach for accurate identification of anticancer peptides by deep learning algorithm","volume":"22","author":"Yu","year":"2020","journal-title":"Mol Ther Nucleic Acids"},{"key":"2026080815384801500_ref32","doi-asserted-by":"publisher","first-page":"102733","DOI":"10.1016\/j.sbi.2023.102733","article-title":"Artificial intelligence-driven antimicrobial peptide discovery","volume":"83","author":"Szymczak","year":"2023","journal-title":"Curr Opin Struct Biol"},{"key":"2026080815384801500_ref33","doi-asserted-by":"publisher","first-page":"138880","DOI":"10.1016\/j.ijbiomac.2024.138880","article-title":"Discovery of anticancer peptides from natural and generated sequences using deep learning","volume":"290","author":"Yue","year":"2025","journal-title":"Int J Biol Macromol"},{"key":"2026080815384801500_ref34","doi-asserted-by":"publisher","first-page":"4684","DOI":"10.1093\/bioinformatics\/btab560","article-title":"Learning embedding features based on multisense-scaled attention architecture to improve the predictive performance of anticancer peptides","volume":"37","author":"He","year":"2021","journal-title":"Bioinformatics"},{"key":"2026080815384801500_ref35","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.1021\/acs.jcim.4c02072","article-title":"ACP-CLB: an anticancer peptide prediction model based on multichannel discriminative processing and integration of large pretrained protein language models","volume":"65","author":"Geng","year":"2025","journal-title":"J Chem Inf Model"},{"key":"2026080815384801500_ref36","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1038\/s41551-021-00689-x","article-title":"Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations","volume":"5","author":"Das","year":"2021","journal-title":"Nat Biomed Eng"},{"key":"2026080815384801500_ref37","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/s41392-022-00904-4","article-title":"Therapeutic peptides: current applications and future directions","volume":"7","author":"Wang","year":"2022","journal-title":"Signal Transduct Target Ther"},{"key":"2026080815384801500_ref38","doi-asserted-by":"publisher","first-page":"7238","DOI":"10.1038\/s41467-022-34904-3","article-title":"AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics","volume":"13","author":"Zeng","year":"2022","journal-title":"Nat Commun"},{"key":"2026080815384801500_ref39","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1002\/med.21658","article-title":"Machine intelligence in peptide therapeutics: a next-generation tool for rapid disease screening","volume":"40","author":"Basith","year":"2020","journal-title":"Med Res Rev"},{"key":"2026080815384801500_ref40","doi-asserted-by":"publisher","first-page":"D837","DOI":"10.1093\/nar\/gku892","article-title":"CancerPPD: a database of anticancer peptides and proteins","volume":"43","author":"Tyagi","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2026080815384801500_ref41","doi-asserted-by":"publisher","first-page":"D523","DOI":"10.1093\/nar\/gkac1052","article-title":"UniProt: the universal protein knowledgebase in 2023","volume":"51","author":"UniProt Consortium","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2026080815384801500_ref42","doi-asserted-by":"publisher","first-page":"1965","DOI":"10.1007\/s11030-023-10602-0","article-title":"LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets","volume":"28","author":"Garai","year":"2024","journal-title":"Mol Divers"},{"key":"2026080815384801500_ref43","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1093\/bioinformatics\/btl158","article-title":"Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences","volume":"22","author":"Li","year":"2006","journal-title":"Bioinformatics"},{"key":"2026080815384801500_ref44","doi-asserted-by":"publisher","first-page":"3150","DOI":"10.1093\/bioinformatics\/bts565","article-title":"CD-HIT: accelerated for clustering the next-generation sequencing data","volume":"28","author":"Fu","year":"2012","journal-title":"Bioinformatics"},{"key":"2026080815384801500_ref45","doi-asserted-by":"publisher","first-page":"btad125","DOI":"10.1093\/bioinformatics\/btad125","article-title":"PreTP-2L: identification of therapeutic peptides and their types using two-layer ensemble learning framework","volume":"39","author":"Yan","year":"2023","journal-title":"Bioinformatics"},{"key":"2026080815384801500_ref46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","article-title":"Feature selection: a data perspective","volume":"53","author":"Li","year":"2020","journal-title":"ACM Comput Surv"},{"key":"2026080815384801500_ref47","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s43586-022-00184-w","article-title":"Principal component analysis","volume":"2","author":"Greenacre","year":"2022","journal-title":"Nat Rev Methods Primers"},{"key":"2026080815384801500_ref48","doi-asserted-by":"publisher","first-page":"2846","DOI":"10.1109\/TPAMI.2019.2932062","article-title":"Switchable normalization for learning-to-normalize deep representation","volume":"41","author":"Luo","year":"2019","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026080815384801500_ref49","doi-asserted-by":"publisher","first-page":"8704","DOI":"10.1109\/TPAMI.2019.2918284","article-title":"Convolutional networks with dense connectivity","volume":"44","author":"Huang","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026080815384801500_ref50","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2026080815384801500_ref51","doi-asserted-by":"publisher","volume-title":"KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA, USA","author":"Chen","DOI":"10.1145\/2939672.2939785"},{"key":"2026080815384801500_ref52","doi-asserted-by":"publisher","first-page":"107965","DOI":"10.1016\/j.patcog.2021.107965","article-title":"A review of methods for imbalanced multi-label classification","volume":"118","author":"Tarekegn","year":"2021","journal-title":"Pattern Recogn"},{"key":"2026080815384801500_ref53","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Netw"},{"key":"2026080815384801500_ref54","first-page":"6638","article-title":"CatBoost: unbiased boosting with categorical features","volume":"31","author":"Prokhorenkova","year":"2018","journal-title":"Adv Neural Inf Proces Syst"},{"key":"2026080815384801500_ref55","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"2026080815384801500_ref56","doi-asserted-by":"publisher","volume-title":"KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage, AK, USA","author":"Akiba","DOI":"10.1145\/3292500.3330701"},{"key":"2026080815384801500_ref57","doi-asserted-by":"publisher","first-page":"e1484","DOI":"10.1002\/widm.1484","article-title":"Hyperparameter optimization: foundations, algorithms, best practices, and open challenges","volume":"13","author":"Bischl","year":"2023","journal-title":"WIREs Data Min Knowl Discov"},{"key":"2026080815384801500_ref58","doi-asserted-by":"publisher","first-page":"3668","DOI":"10.1109\/TCYB.2019.2950779","article-title":"A survey of optimization methods from a machine learning perspective","volume":"50","author":"Sun","year":"2020","journal-title":"IEEE Trans Cybern"},{"key":"2026080815384801500_ref59","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"Chicco","year":"2020","journal-title":"BMC Genomics"},{"key":"2026080815384801500_ref60","doi-asserted-by":"publisher","first-page":"7955","DOI":"10.1109\/TPAMI.2021.3119334","article-title":"The emerging trends of multi-label learning","volume":"44","author":"Liu","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026080815384801500_ref61","doi-asserted-by":"publisher","first-page":"5481","DOI":"10.5194\/gmd-15-5481-2022","article-title":"Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not","volume":"15","author":"Hodson TO","year":"2022","journal-title":"Geosci Model Dev"},{"key":"2026080815384801500_ref62","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2026080815384801500_ref63","doi-asserted-by":"publisher","first-page":"4245","DOI":"10.3390\/molecules25184245","article-title":"Peptides with dual antimicrobial-anticancer activity: strategies to overcome peptide limitations and rational design of anticancer peptides","volume":"25","author":"Liscano","year":"2020","journal-title":"Molecules"},{"key":"2026080815384801500_ref64","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2026080815384801500_ref65","doi-asserted-by":"publisher","first-page":"4473","DOI":"10.1016\/j.csbj.2022.07.043","article-title":"MLACP 2.0: an updated machine learning tool for anticancer peptide prediction","volume":"20","author":"Phan","year":"2022","journal-title":"Comput Struct Biotechnol J"},{"key":"2026080815384801500_ref66","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"Greener","year":"2021","journal-title":"Nat Rev Mol Cell Biol"},{"key":"2026080815384801500_ref67","doi-asserted-by":"publisher","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","article-title":"Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences","volume":"118","author":"Rives","year":"2021","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026080815384801500_ref68","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s12539-024-00612-3","article-title":"Machine learning accelerates de novo design of antimicrobial peptides","volume":"16","author":"Yin","year":"2024","journal-title":"Interdiscip Sci: Comput Life Sci"},{"key":"2026080815384801500_ref69","doi-asserted-by":"publisher","first-page":"8852","DOI":"10.1073\/pnas.1901979116","article-title":"Machine learning-assisted directed protein evolution with combinatorial libraries","volume":"116","author":"Wu","year":"2019","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026080815384801500_ref70","doi-asserted-by":"publisher","first-page":"4131","DOI":"10.1021\/acs.jcim.9b00628","article-title":"Graph convolutional neural networks for predicting drug-target interactions","volume":"59","author":"Torng","year":"2019","journal-title":"J Chem Inf Model"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/4\/bbag424\/70558977\/bbag424.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/4\/bbag424\/70558977\/bbag424.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T19:38:54Z","timestamp":1786217934000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag424\/8757001"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":70,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag424","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,7]]},"published":{"date-parts":[[2026,7]]},"article-number":"bbag424"}}