{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T18:58:06Z","timestamp":1765479486053,"version":"3.48.0"},"reference-count":140,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National KeyR&amp;D Program of China","award":["2020YFA0713601"],"award-info":[{"award-number":["2020YFA0713601"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput. Biol. Bioinform."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tcbbio.2025.3627203","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T17:11:19Z","timestamp":1761930679000},"page":"3419-3437","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Driven Protein-Ligand Binding Affinity Prediction: Data, Architecture, Training and Evaluation"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0464-748X","authenticated-orcid":false,"given":"Zhiwei","family":"Li","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5861-8565","authenticated-orcid":false,"given":"Guoqiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0868-9554","authenticated-orcid":false,"given":"Haoran","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1720-6589","authenticated-orcid":false,"given":"Jiacong","family":"Mi","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4933-0081","authenticated-orcid":false,"given":"Jiahua","family":"Shi","sequence":"additional","affiliation":[{"name":"Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9403-7140","authenticated-orcid":false,"given":"Jun","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.biophys.36.040306.132550"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-020-00289-y"},{"issue":"1","key":"ref3","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/nprot.2006.28","article-title":"Isothermal titration calorimetry to determine association constants for high-affinity ligands","volume":"1","author":"Velazquez-Campoy and","journal-title":"Nat. Protoc."},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1016\/j.trac.2024.117716","article-title":"Microscale thermophoresis (MST) and spectral shift (SpS) in drug discovery","volume":"176","author":"Nowak","year":"2024","journal-title":"TrAC Trends Anal. Chem."},{"issue":"5337","key":"ref5","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1126\/science.278.5337.497","article-title":"Discovering high-affinity ligands for proteins","volume":"278","author":"Hajduk","year":"1996","journal-title":"Science"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1021\/ci500731a"},{"issue":"3","key":"ref7","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1006\/jmbi.1996.0897","article-title":"Development and validation of a genetic algorithm for flexible docking","volume":"267","author":"Jones","year":"1997","journal-title":"J. Mol. Biol."},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1021\/jm0306430"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq112"},{"key":"ref10","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129303","article-title":"BoostSF-SHAP: Gradient boosting-based software for proteinligand binding affinity prediction with explanations","volume":"622","author":"Chen","year":"2025","journal-title":"Neurocomputing"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1002\/pro.5257"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1023\/a:1016357811882"},{"issue":"7873","key":"ref13","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"issue":"6","key":"ref14","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1038\/s42256-022-00499-z","article-title":"Controllable protein design with language models","volume":"4","author":"Ferruz","year":"2022","journal-title":"Nature Mach. Intell."},{"key":"ref15","first-page":"12489","article-title":"Protein design with guided discrete diffusion","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gruver"},{"key":"ref16","first-page":"42317","article-title":"Structure-informed language models are protein designers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zheng"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01275-4"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2213149120"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i1.32019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00965-w"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03211-3"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-024-01302-7"},{"article-title":"Scaling laws for neural language models","year":"2020","author":"Kaplan","key":"ref23"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1429"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1111\/j.1432-1033.1977.tb11885.x"},{"key":"ref26","first-page":"1521","article-title":"Unbiased look at dataset bias","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Torralba"},{"article-title":"Dont take the easy way out: Ensemble based methods for avoiding known dataset biases","year":"2019","author":"Clark","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1021\/jm030580l"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1021\/ci9000053"},{"issue":"11","key":"ref30","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nat. Biotechnol."},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1021\/ci200082t"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1021\/ci400709d"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1021\/jm061277y"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkl999"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1021\/jm300687e"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr777"},{"issue":"15","key":"ref37","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.drudis.2010.05.015","article-title":"Druggable pockets and binding site centric chemical space: A paradigm shift in drug discovery","volume":"15","author":"Prot","year":"2010","journal-title":"Drug Discov. Today"},{"article-title":"Leak proof PDBBind: A reorganized dataset of protein-ligand complexes for more generalizable binding affinity prediction","year":"2024","author":"Li","key":"ref38"},{"key":"ref39","doi-asserted-by":"crossref","first-page":"82146","DOI":"10.1109\/ACCESS.2021.3084358","article-title":"A survey on semi-, self- and unsupervised learning for image classification","volume":"9","author":"Schmarje","year":"2021","journal-title":"IEEE Access"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-024-10134-2"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09825-6"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00876-4"},{"key":"ref43","first-page":"6000","article-title":"Attention is all you need","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Vaswani"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01199"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"issue":"66","key":"ref47","article-title":"Dimensionality reduction: A comparative review","volume":"10","author":"Van Der Maaten","year":"2007","journal-title":"J. Mach. Learn."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty593"},{"first-page":"64","volume-title":"Proc. 2019 IEEE Int. Conf. Bioinf. Biomed.","author":"Zhao","key":"ref50"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz111"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab072"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu352"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00650"},{"issue":"22","key":"ref55","doi-asserted-by":"crossref","DOI":"10.3390\/ijms21228424","article-title":"AK-score: Accurate protein-ligand binding affinity prediction using an ensemble of 3d-convolutional neural networks","volume":"21","author":"Kwon","year":"2020","journal-title":"Int. J. Mol. Sci."},{"key":"ref56","first-page":"303","article-title":"DeepAtom: A framework for protein-ligand binding affinity prediction","volume-title":"Proc. 2019 IEEE Int. Conf. Bioinf. Biomed","author":"Li"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty374"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1177\/11779322211030364"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab474"},{"key":"ref60","article-title":"OctSurf: Efficient hierarchical voxel-based molecular surface representation for protein-ligand affinity prediction","volume":"105","author":"Liu","year":"2021","journal-title":"J. Mol. Graph. Modelling"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa921"},{"issue":"12","key":"ref62","doi-asserted-by":"crossref","first-page":"8191","DOI":"10.1109\/TPAMI.2024.3400515","article-title":"Interaction-based inductive bias in graph neural networks: Enhancing protein-ligand binding affinity predictions from 3D structures","volume":"46","author":"Yang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"35","key":"ref63","first-page":"20701","article-title":"Drugtarget affinity prediction using graph neural network and contact maps","volume":"10","author":"Jiang","year":"2020","journal-title":"BMC Bioinf."},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty1036"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.9b04162"},{"key":"ref66","article-title":"Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity","volume-title":"SIGN","author":"Li","year":"2024"},{"issue":"4","key":"ref67","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbae333","article-title":"GEMF: A novel geometry-enhanced mid-fusion network for PLA prediction","volume":"25","author":"Zhou","year":"2024","journal-title":"Brief. Bioinf."},{"issue":"12","key":"ref68","doi-asserted-by":"crossref","first-page":"18370","DOI":"10.1109\/TNNLS.2023.3314928","article-title":"Structure-aware graph attention diffusion network for proteinligand binding affinity prediction","volume":"35","author":"Li","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c01961"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c00253"},{"key":"ref71","first-page":"9323","article-title":"E(n) equivariant graph neural networks","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Satorras","year":"2021"},{"issue":"7","key":"ref72","doi-asserted-by":"crossref","first-page":"4336","DOI":"10.1109\/JBHI.2024.3383245","article-title":"Equivariant line graph neural network for protein-ligand binding affinity prediction","volume":"28","author":"Yi","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8","key":"ref73","doi-asserted-by":"crossref","first-page":"4343","DOI":"10.1039\/C9CP06554G","article-title":"A review of mathematical representations of biomolecular data","volume":"22","author":"Nguyen","year":"2020","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10146-z"},{"key":"ref75","doi-asserted-by":"crossref","DOI":"10.1101\/606202","article-title":"Deciphering interaction fingerprints from protein molecular surfaces","author":"Gainza","year":"2019"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005690"},{"issue":"3","key":"ref77","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1039\/D1SC05180F","article-title":"MGraphDTA: Deep multiscale graph neural network for explainable drugtarget binding affinity prediction","volume":"13","author":"Yang","year":"2022","journal-title":"Chem. Sci."},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-024-00938-6"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c00251"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae413"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.4c01290"},{"issue":"8","key":"ref82","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/JBHI.2024.3350666","article-title":"GraphCL-DTA: A graph contrastive learning with molecular semantics for drug-target binding affinity prediction","volume":"28","author":"Yang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"ref83","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.jfds.2017.05.001","article-title":"An overview on data representation learning: From traditional feature learning to recent deep learning","volume":"2","author":"Zhong","year":"2026","journal-title":"J. Finance Data Sci."},{"issue":"55","key":"ref84","first-page":"1","article-title":"Neural architecture search: A survey","volume":"20","author":"Elsken","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1145\/3447582"},{"issue":"2","key":"ref86","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Baltrusaitis","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c00567"},{"key":"ref88","doi-asserted-by":"crossref","DOI":"10.3410\/B3-19","article-title":"Conformational selection or induced fit? 50 years of debate resolved","volume":"3","author":"Changeux","year":"2011","journal-title":"F1000 Biol. Rep."},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa544"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.1c01830"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac603"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpclett.2c03906"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad049"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad340"},{"key":"ref95","doi-asserted-by":"crossref","first-page":"4396","DOI":"10.1016\/j.csbj.2024.11.050","article-title":"MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced proteinligand binding affinity prediction","volume":"23","author":"Liu","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"issue":"16","key":"ref96","doi-asserted-by":"crossref","first-page":"12880","DOI":"10.1039\/D3CP05664C","article-title":"SadNet: A novel multimodal fusion network for proteinligand binding affinity prediction","volume":"26","author":"Hong","year":"2024","journal-title":"Phys. Chem. Chem. Phys."},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.4c01828"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-024-10326-x"},{"key":"ref99","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2019.01243","article-title":"GANsDTA: Predicting drug-target binding affinity using GANs","volume":"10","author":"Zhao","year":"2020","journal-title":"Front Genet."},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad145"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab506"},{"key":"ref102","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. 13th Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref103","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification","volume-title":"Proc. IEEE Int. Conf. Comput. Vis.","author":"He"},{"issue":"3","key":"ref104","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1214\/aoms\/1177729392","article-title":"Stochastic estimation of the maximum of a regression function","volume":"23","author":"Kiefer","year":"1952","journal-title":"Ann. Math. Statist."},{"issue":"5","key":"ref105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0041-5553(64)90137-5","article-title":"Some methods of speeding up the convergence of iteration methods","volume":"4","author":"Polyak","year":"1964","journal-title":"USSR Comput. Math. Math. Phys."},{"issue":"7","key":"ref106","first-page":"121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"article-title":"Adam: A method for stochastic optimization","year":"2015","author":"Kingma","key":"ref107"},{"key":"ref108","article-title":"SE-OnionNet: A convolution neural network for proteinligand binding affinity prediction","volume":"11","author":"Wang","year":"2021"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09784-7"},{"key":"ref110","first-page":"24392","article-title":"Understanding and improving early stopping for learning with noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bai"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad462"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32236-6_51"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1145\/3561048"},{"issue":"10","key":"ref114","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s42256-020-00236-4","article-title":"Drug discovery with explainable artificial intelligence","volume":"2","author":"Jim","year":"2020","journal-title":"Nature Mach. Intell."},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"article-title":"Intriguing properties of neural networks","year":"2023","author":"Szegedy","key":"ref116"},{"issue":"6","key":"ref117","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1038\/s42256-024-00852-4","article-title":"Challenges, evaluation and opportunities for open-world learning","volume":"6","author":"Kejriwal","year":"2024","journal-title":"Nature Mach. Intell."},{"key":"ref118","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz183"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.21334"},{"key":"ref120","first-page":"77","article-title":"Pointnet: Deep learning on point sets for 3D classification and segmentation","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Qi"},{"key":"ref121","doi-asserted-by":"crossref","DOI":"10.24963\/ijcai.2021\/214","article-title":"Masked label prediction: Unified message passing model for semi-supervised classification","author":"Shi","year":"2021"},{"article-title":"Learning from protein structure with geometric vector perceptrons","year":"2021","author":"Jing","key":"ref122"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3248160"},{"issue":"4","key":"ref124","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1038\/s41578-020-00269-6","article-title":"Targeted drug delivery strategies for precision medicines","volume":"6","author":"Manzari","year":"2021","journal-title":"Nat. Rev. Mater."},{"issue":"2","key":"ref125","article-title":"Structural insights into SARS-CoV-2 proteins","volume":"433","author":"Arya","year":"2021","journal-title":"Nat. Rev. Mater."},{"issue":"3","key":"ref126","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1038\/nrmicro.2015.24","article-title":"Synthetic biology to access and expand natures chemical diversity","volume":"14","author":"Smanski","year":"2016","journal-title":"Nat. Rev. Microbiol."},{"issue":"23","key":"ref127","first-page":"6396","article-title":"Synthetic biologythe synthesis of biology","volume":"56","author":"Auslnder","year":"2024","journal-title":"Biol. Res."},{"key":"ref128","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2016.00011","article-title":"Recent advances in biosensor technology for potential applicationsan overview","volume":"4","author":"Vigneshvar","year":"2016","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref129","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.bios.2016.12.014","article-title":"Recent advances in biosensor development for the detection of cancer biomarkers","volume":"91","author":"Jayanthi","year":"2017","journal-title":"Biosensors Bioelectron."},{"article-title":"Directional message passing for molecular graphs","year":"2021","author":"Gasteiger","key":"ref130"},{"article-title":"Spherical message passing for 3D graph networks","year":"2022","author":"Liu","key":"ref131"},{"key":"ref132","first-page":"650","article-title":"ComENet: Towards complete and efficient message passing for 3D molecular graphs","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1021\/cr4004665"},{"issue":"6","key":"ref134","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/TCBB.2018.2827029","article-title":"Predicting hospital readmission via cost-sensitive deep learning","volume":"15","author":"Wang","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0192-5"},{"issue":"2","key":"ref136","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1016\/j.acha.2019.06.004","article-title":"Universality of deep convolutional neural networks","volume":"48","author":"Zhou","year":"2020","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref137","first-page":"12695","article-title":"What makes training multi-modal classification networks hard?","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Wang"},{"key":"ref138","doi-asserted-by":"publisher","DOI":"10.4155\/fmc.12.4"},{"issue":"5","key":"ref139","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/nrd.2018.21","article-title":"Kinase inhibitors: The road ahead","volume":"17","author":"Ferguson","year":"2018","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref140","first-page":"427","article-title":"Deep neural networks are easily fooled: High confidence predictions for unrecognizable images","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Nguyen"}],"container-title":["IEEE Transactions on Computational Biology and Bioinformatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10723156\/11296745\/11223139.pdf?arnumber=11223139","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T18:45:00Z","timestamp":1765478700000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11223139\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":140,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tcbbio.2025.3627203","relation":{},"ISSN":["2998-4165"],"issn-type":[{"type":"electronic","value":"2998-4165"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}