{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:23:08Z","timestamp":1784118188025,"version":"3.55.0"},"reference-count":55,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T00:00:00Z","timestamp":1682380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22173038"],"award-info":[{"award-number":["22173038"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Supercomputing Center of Lanzhou University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Rapid and accurate prediction of drug-target affinity can accelerate and improve the drug discovery process. Recent studies show that deep learning models may have the potential to provide fast and accurate drug-target affinity prediction. However, the existing deep learning models still have their own disadvantages that make it difficult to complete the task satisfactorily. Complex-based models rely heavily on the time-consuming docking process, and complex-free models lacks interpretability. In this study, we introduced a novel knowledge-distillation insights drug-target affinity prediction model with feature fusion inputs to make fast, accurate and explainable predictions. We benchmarked the model on public affinity prediction and virtual screening dataset. The results show that it outperformed previous state-of-the-art models and achieved comparable performance to previous complex-based models. Finally, we study the interpretability of this model through visualization and find it can provide meaningful explanations for pairwise interaction. We believe this model can further improve the drug-target affinity prediction for its higher accuracy and reliable interpretability.<\/jats:p>","DOI":"10.1093\/bib\/bbad145","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T18:37:48Z","timestamp":1682534268000},"source":"Crossref","is-referenced-by-count":24,"title":["Improving drug-target affinity prediction via feature fusion and knowledge distillation"],"prefix":"10.1093","volume":"24","author":[{"given":"Ruiqiang","family":"Lu","sequence":"first","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Lanzhou University , 730000 Gansu , China"},{"name":"Ping An Healthcare Technology , 100027 Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology , 100027 Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , 710126 Shaanxi , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuquan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Lanzhou University , 730000 Gansu , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuoyan","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Lanzhou University , 730000 Gansu , China"},{"name":"Ping An Healthcare Technology , 100027 Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiting","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Lanzhou University , 730000 Gansu , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Applied Science, Macao Polytechnic University , 999078 Macau , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology , 100027 Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guotong","family":"Xie","sequence":"additional","affiliation":[{"name":"Ping An Healthcare Technology , 100027 Beijing , China"},{"name":"Ping An Health Cloud Company Limited , 100027 Beijing , China"},{"name":"Ping An International Smart City Technology Co., Ltd. , 100027 Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Lanzhou University , 730000 Gansu , China"},{"name":"State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology , 999078 Macau , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,4,25]]},"reference":[{"issue":"17","key":"2023052022215479500_ref1","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"Deepdta: deep drug\u2013target binding affinity prediction","volume":"34","author":"\u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"issue":"4","key":"2023052022215479500_ref2","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/S1367-5931(00)00110-1","article-title":"High-throughput screening: new technology for the 21st century","volume":"4","author":"Hertzberg","year":"2000","journal-title":"Curr Opin Chem Biol"},{"issue":"7","key":"2023052022215479500_ref3","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1021\/jm0306430","article-title":"Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy","volume":"47","author":"Friesner","year":"2004","journal-title":"J Med Chem"},{"issue":"3","key":"2023052022215479500_ref4","doi-asserted-by":"crossref","first-page":"bbaa070","DOI":"10.1093\/bib\/bbaa070","article-title":"Beware of the generic machine learning-based scoring functions in structure-based virtual screening","volume":"22","author":"Shen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023052022215479500_ref5","first-page":"975","article-title":"Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity","volume-title":"SIGKDD","author":"Li","year":"2021"},{"issue":"24","key":"2023052022215479500_ref6","doi-asserted-by":"crossref","first-page":"18209","DOI":"10.1021\/acs.jmedchem.1c01830","article-title":"Interactiongraphnet: a novel and efficient deep graph representation learning framework for accurate protein\u2013ligand interaction predictions","volume":"64","author":"Jiang","year":"2021","journal-title":"J Med Chem"},{"issue":"2","key":"2023052022215479500_ref7","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1021\/acs.jcim.7b00650","article-title":"K deep: protein\u2013ligand absolute binding affinity prediction via 3d-convolutional neural networks","volume":"58","author":"Jim\u00e9nez","year":"2018","journal-title":"J Chem Inf Model"},{"issue":"14","key":"2023052022215479500_ref8","doi-asserted-by":"crossref","first-page":"15956","DOI":"10.1021\/acsomega.9b01997","article-title":"Onionnet: a multiple-layer intermolecular-contact-based convolutional neural network for protein\u2013ligand binding affinity prediction","volume":"4","author":"Zheng","year":"2019","journal-title":"ACS omega"},{"key":"2023052022215479500_ref9","doi-asserted-by":"crossref","first-page":"7946","DOI":"10.1021\/acs.jmedchem.2c00487","article-title":"On the frustration to predict binding affinities from protein\u2013ligand structures with deep neural networks","volume":"65","author":"Volkov","year":"2022","journal-title":"J Med Chem"},{"issue":"9","key":"2023052022215479500_ref10","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1021\/acs.jcim.9b00387","article-title":"Predicting drug\u2013target interaction using a novel graph neural network with 3d structure-embedded graph representation","volume":"59","author":"Lim","year":"2019","journal-title":"J Chem Inf Model"},{"issue":"16","key":"2023052022215479500_ref11","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1093\/bioinformatics\/btaa524","article-title":"Transformercpi: improving compound\u2013protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"issue":"2","key":"2023052022215479500_ref12","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s42256-020-0152-y","article-title":"Predicting drug\u2013protein interaction using quasi-visual question answering system","volume":"2","author":"Zheng","year":"2020","journal-title":"Nat Mach Intell"},{"issue":"8","key":"2023052022215479500_ref13","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"Graphdta: predicting drug\u2013target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2021","journal-title":"Bioinformatics"},{"issue":"21","key":"2023052022215479500_ref14","doi-asserted-by":"crossref","first-page":"3666","DOI":"10.1093\/bioinformatics\/bty374","article-title":"Development and evaluation of a deep learning model for protein\u2013ligand binding affinity prediction","volume":"34","author":"Stepniewska-Dziubinska","year":"2018","journal-title":"Bioinformatics"},{"issue":"2","key":"2023052022215479500_ref15","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TCBB.2021.3094217","article-title":"Gefa: early fusion approach in drug-target affinity prediction","volume":"19","author":"Nguyen","year":"2021","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"2023052022215479500_ref16","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023052022215479500_ref17","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown","year":"2020","journal-title":"NeurIPS"},{"key":"2023052022215479500_ref18","article-title":"Bert: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv preprint arXiv:181004805"},{"key":"2023052022215479500_ref19","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong","year":"2020","journal-title":"Adv Neural Inform Process Syst"},{"issue":"6","key":"2023052022215479500_ref20","doi-asserted-by":"crossref","first-page":"bbab109","DOI":"10.1093\/bib\/bbab109","article-title":"An effective self-supervised framework for learning expressive molecular global representations to drug discovery","volume":"22","author":"Li","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"2023052022215479500_ref21","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2023052022215479500_ref22","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv preprint arXiv:160902907"},{"key":"2023052022215479500_ref23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ddtec.2020.11.009","article-title":"A compact review of molecular property prediction with graph neural networks","volume":"37","author":"Wieder","year":"2020","journal-title":"Drug Discov Today Technol"},{"issue":"7873","key":"2023052022215479500_ref24","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"},{"key":"2023052022215479500_ref25","article-title":"Diffdock: diffusion steps, twists, and turns for molecular docking","author":"Corso","year":"2022","journal-title":"arXiv preprint arXiv:221001776"},{"key":"2023052022215479500_ref26","first-page":"20503","article-title":"Equibind: Geometric deep learning for drug binding structure prediction","volume-title":"International Conference on Machine Learning","author":"St\u00e4rk","year":"2022"},{"key":"2023052022215479500_ref27","article-title":"Independent se (3)-equivariant models for end-to-end rigid protein docking","author":"Ganea","year":"2021","journal-title":"arXiv preprint arXiv:211107786"},{"issue":"6","key":"2023052022215479500_ref28","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: a survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int J Comput Vis"},{"issue":"7","key":"2023052022215479500_ref29","article-title":"Distilling the knowledge in a neural network","volume":"2","author":"Hinton","year":"2015","journal-title":"arXiv preprint arXiv:150302531"},{"issue":"18","key":"2023052022215479500_ref30","doi-asserted-by":"crossref","first-page":"10520","DOI":"10.1021\/acs.chemrev.8b00728","article-title":"Concepts of artificial intelligence for computer-assisted drug discovery","volume":"119","author":"Yang","year":"2019","journal-title":"Chem Rev"},{"issue":"12","key":"2023052022215479500_ref31","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1038\/s42256-021-00418-8","article-title":"Geometric deep learning on molecular representations","volume":"3","author":"Atz","year":"2021","journal-title":"Nat Mach Intell"},{"key":"2023052022215479500_ref32","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"2023052022215479500_ref33","article-title":"RDKit: open-source cheminformatics"},{"issue":"1","key":"2023052022215479500_ref34","first-page":"1","article-title":"Open babel: an open chemical toolbox","volume":"3","author":"O\u2019Boyle","year":"2011","journal-title":"J Chem"},{"key":"2023052022215479500_ref35","article-title":"Pointnet++: deep hierarchical feature learning on point sets in a metric space","volume":"30","author":"Qi","year":"2017","journal-title":"NeurIPS"},{"issue":"1","key":"2023052022215479500_ref36","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1021\/ct300857j","article-title":"Openmm 4: a reusable, extensible, hardware independent library for high performance molecular simulation","volume":"9","author":"Eastman","year":"2013","journal-title":"J Chem Theory Comput"},{"key":"2023052022215479500_ref37","volume-title":"Pymol","author":"Schr\u00f6dinger"},{"key":"2023052022215479500_ref38","article-title":"Graphein-a python library for geometric deep learning and network analysis on protein structures","author":"Jamasb","year":"2020","journal-title":"bioRxiv"},{"issue":"4","key":"2023052022215479500_ref39","doi-asserted-by":"crossref","first-page":"bbaa266","DOI":"10.1093\/bib\/bbaa266","article-title":"Trimnet: learning molecular representation from triplet messages for biomedicine","volume":"22","author":"Li","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023052022215479500_ref40","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume-title":"Handbook Brain Theory Neural Netw","author":"LeCun","year":"1995"},{"key":"2023052022215479500_ref41","first-page":"9323","article-title":"E (n) equivariant graph neural networks","volume-title":"ICML","author":"Satorras","year":"2021"},{"issue":"12","key":"2023052022215479500_ref42","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1038\/s42256-021-00409-9","article-title":"A geometric deep learning approach to predict binding conformations of bioactive molecules","volume":"3","author":"M\u00e9ndez-Lucio","year":"2021","journal-title":"Nat Mach Intell"},{"key":"2023052022215479500_ref43","doi-asserted-by":"crossref","DOI":"10.1145\/1015330.1015332","article-title":"Solving large scale linear prediction problems using stochastic gradient descent algorithms","volume-title":"ICML","author":"Zhang","year":"2004"},{"key":"2023052022215479500_ref44","doi-asserted-by":"crossref","first-page":"924","DOI":"10.3389\/fphar.2019.00924","article-title":"Improving the virtual screening ability of target-specific scoring functions using deep learning methods","volume":"10","author":"Wang","year":"2019","journal-title":"Front Pharmacol"},{"issue":"12","key":"2023052022215479500_ref45","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.1021\/jm030580l","article-title":"The pdbbind database: collection of binding affinities for protein- ligand complexes with known three-dimensional structures","volume":"47","author":"Wang","year":"2004","journal-title":"J Med Chem"},{"issue":"2","key":"2023052022215479500_ref46","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1021\/acs.jcim.8b00545","article-title":"Comparative assessment of scoring functions: the casf-2016 update","volume":"59","author":"Su","year":"2018","journal-title":"J Chem Inf Model"},{"issue":"6","key":"2023052022215479500_ref47","doi-asserted-by":"crossref","first-page":"bbab136","DOI":"10.1093\/bib\/bbab136","article-title":"Forman persistent ricci curvature (fprc)-based machine learning models for protein\u2013ligand binding affinity prediction","volume":"22","author":"Wee","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1","key":"2023052022215479500_ref48","doi-asserted-by":"crossref","first-page":"e1005929","DOI":"10.1371\/journal.pcbi.1005929","article-title":"Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening","volume":"14","author":"Cang","year":"2018","journal-title":"PLoS Comput Biol"},{"issue":"9","key":"2023052022215479500_ref49","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1093\/bioinformatics\/btq112","article-title":"A machine learning approach to predicting protein\u2013ligand binding affinity with applications to molecular docking","volume":"26","author":"Ballester","year":"2010","journal-title":"Bioinformatics"},{"issue":"6","key":"2023052022215479500_ref50","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","article-title":"Moltrans: molecular interaction transformer for drug\u2013target interaction prediction","volume":"37","author":"Huang","year":"2021","journal-title":"Bioinformatics"},{"issue":"5","key":"2023052022215479500_ref51","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1021\/acs.jcim.2c00060","article-title":"Structure-aware multimodal deep learning for drug\u2013protein interaction prediction","volume":"62","author":"Wang","year":"2022","journal-title":"J Chem Inf Model"},{"issue":"14","key":"2023052022215479500_ref52","doi-asserted-by":"crossref","first-page":"6582","DOI":"10.1021\/jm300687e","article-title":"Directory of useful decoys, enhanced (dud-e): better ligands and decoys for better benchmarking","volume":"55","author":"Mysinger","year":"2012","journal-title":"J Med Chem"},{"issue":"6","key":"2023052022215479500_ref53","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1021\/ci400115b","article-title":"Evaluation and optimization of virtual screening workflows with dekois 2.0\u2013a public library of challenging docking benchmark sets","volume":"53","author":"Bauer","year":"2013","journal-title":"J Chem Inf Model"},{"issue":"9","key":"2023052022215479500_ref54","doi-asserted-by":"crossref","first-page":"4263","DOI":"10.1021\/acs.jcim.0c00155","article-title":"Lit-pcba: an unbiased data set for machine learning and virtual screening","volume":"60","author":"Tran-Nguyen","year":"2020","journal-title":"J Chem Inf Model"},{"issue":"1","key":"2023052022215479500_ref55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep46710","article-title":"Performance of machine-learning scoring functions in structure-based virtual screening","volume":"7","author":"W\u00f3jcikowski","year":"2017","journal-title":"Sci Rep"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/3\/bbad145\/50410960\/bbad145.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/3\/bbad145\/50410960\/bbad145.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,20]],"date-time":"2023-05-20T22:22:56Z","timestamp":1684621376000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad145\/7142721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,25]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,5,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad145","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,5]]},"published":{"date-parts":[[2023,4,25]]},"article-number":"bbad145"}}