{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T17:44:28Z","timestamp":1769881468313,"version":"3.49.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning\u2019s strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\\%$, thereby significantly accelerating the process of drug-like peptide generation.<\/jats:p>","DOI":"10.1093\/bib\/bbae444","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T01:36:59Z","timestamp":1726018619000},"source":"Crossref","is-referenced-by-count":13,"title":["Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1310-4656","authenticated-orcid":false,"given":"Qian","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , 238 Songling Rd, 266100 Shandong, China"}]},{"given":"Xiaotong","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , 238 Songling Rd, 266100 Shandong, China"}]},{"given":"Zhiqiang","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , 238 Songling Rd, 266100 Shandong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6553-6205","authenticated-orcid":false,"given":"Hao","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , 238 Songling Rd, 266100 Shandong, China"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China , 238 Songling Rd, 266100 Shandong, China"}]}],"member":"286","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"2024091101364823500_ref1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0065-3233(08)60375-7","article-title":"Design of peptides and proteins[J]","volume":"39","author":"Degrado","year":"1988","journal-title":"Adv Protein Chem"},{"key":"2024091101364823500_ref2","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1080\/07388551.2020.1796576","article-title":"Antimicrobial peptides as therapeutic agents: opportunities and challenges[J]","volume":"40","author":"Mahlapuu","year":"2020","journal-title":"Crit Rev Biotechnol"},{"key":"2024091101364823500_ref3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3390\/antibiotics9010024","article-title":"Development and challenges of antimicrobial peptides for therapeutic applications[J]","volume":"9","author":"Chen","year":"2020","journal-title":"Antibiotics"},{"key":"2024091101364823500_ref4","doi-asserted-by":"crossref","first-page":"447","DOI":"10.2174\/138920308785915209","article-title":"Past and future perspectives of synthetic peptide libraries[J]","volume":"9","author":"Marasco","year":"2008","journal-title":"Curr Protein Pept Sci"},{"key":"2024091101364823500_ref5","doi-asserted-by":"crossref","first-page":"4373","DOI":"10.3390\/molecules18044373","article-title":"Chemical methods for peptide and protein production[J]","volume":"18","author":"Chandrudu","year":"2013","journal-title":"Molecules"},{"key":"2024091101364823500_ref6","doi-asserted-by":"crossref","first-page":"411","DOI":"10.2174\/1385272024604970","article-title":"Current synthetic approaches to peptide and peptidomimetic cyclization[J]","volume":"6","author":"Li","year":"2002","journal-title":"Curr Org Chem"},{"key":"2024091101364823500_ref7","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1016\/j.bmc.2018.01.012","article-title":"Peptide chemistry toolbox\u2013transforming natural peptides into peptide therapeutics[J]","volume":"26","author":"Erak","year":"2018","journal-title":"Bioorg Med Chem"},{"key":"2024091101364823500_ref8","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1002\/prot.10584","article-title":"Quantifying the effect of burial of amino acid residues on protein stability[J]","volume":"54","author":"Zhou","year":"2004","journal-title":"Proteins"},{"key":"2024091101364823500_ref9","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/304076a0","article-title":"Single amino acid substitutions in influenza haemagglutinin change receptor binding specificity[J]","volume":"304","author":"Rogers","year":"1983","journal-title":"Nature"},{"key":"2024091101364823500_ref10","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1021\/acs.jcim.2c01485","article-title":"Deep learning-based bioactive therapeutic peptide generation and screening[J]","volume":"63","author":"Zhang","year":"2023","journal-title":"J Chem Inf Model"},{"key":"2024091101364823500_ref11","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1039\/D1DD00024A","article-title":"Deep generative models for peptide design[J]","volume":"1","author":"Wan","year":"2022","journal-title":"Digit Discov"},{"key":"2024091101364823500_ref12","first-page":"1","article-title":"Deep reinforcement learning and docking simulations for autonomous molecule generation in de novo drug design","volume-title":"Proceedings of the 3rd ACM International Conference on Multimedia in Asia","author":"Wang","year":"2021"},{"key":"2024091101364823500_ref13","doi-asserted-by":"crossref","first-page":"15519","DOI":"10.1021\/jacs.2c03858","article-title":"Pseudo-isolated $\\alpha $-helix platform for the recognition of deep and narrow targets","volume":"144","author":"Kim","year":"2022","journal-title":"J Am Chem Soc"},{"key":"2024091101364823500_ref14","doi-asserted-by":"crossref","first-page":"e12283","DOI":"10.1016\/j.heliyon.2022.e12283","article-title":"Peptide utility (PU) search server: a new tool for peptide sequence search from multiple databases[J]","volume":"8","author":"Chamoli","year":"2022","journal-title":"Heliyon"},{"key":"2024091101364823500_ref15","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.ijbiomac.2022.07.103","article-title":"In pursuit of next-generation therapeutics: antimicrobial peptides against superbugs, their sources, mechanism of action, nanotechnology-based delivery, and clinical applications[J]","volume":"218","author":"Thakur","year":"2022","journal-title":"Int J Biol Macromol"},{"key":"2024091101364823500_ref16","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1021\/acs.jcim.7b00414","article-title":"Recurrent neural network model for constructive peptide design[J]","volume":"58","author":"Muller","year":"2018","journal-title":"J Chem Inf Model"},{"key":"2024091101364823500_ref17","doi-asserted-by":"crossref","first-page":"411","DOI":"10.3390\/antibiotics11030411","article-title":"Novel antimicrobial peptides designed using a recurrent neural network reduce mortality in experimental sepsis[J]","volume":"11","author":"Bolatchiev","year":"2022","journal-title":"Antibiotics"},{"key":"2024091101364823500_ref18","doi-asserted-by":"crossref","first-page":"2961","DOI":"10.1021\/acs.jcim.2c00526","article-title":"Sequential properties representation scheme for recurrent neural network-based prediction of therapeutic peptides[J]","volume":"62","author":"Otovic","year":"2022","journal-title":"J Chem Inf Model"},{"key":"2024091101364823500_ref19","doi-asserted-by":"crossref","first-page":"4825","DOI":"10.1016\/j.csbj.2023.09.038","article-title":"Accelerated NLRP3 inflammasome-inhibitory peptide design using a recurrent neural network model and molecular dynamics simulations[J]","volume":"21","author":"Ahmad","year":"2023","journal-title":"Comput Struct Biotechnol J"},{"key":"2024091101364823500_ref20","doi-asserted-by":"crossref","first-page":"20746","DOI":"10.1021\/acsomega.0c00442","article-title":"Variational autoencoder for generation of antimicrobial peptides[J]","volume":"5","author":"Dean","year":"2020","journal-title":"ACS Omega"},{"key":"2024091101364823500_ref21","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1039\/D2DD00091A","article-title":"Latent spaces for antimicrobial peptide design[J]","volume":"2","author":"Renaud","year":"2023","journal-title":"Digit Discov"},{"key":"2024091101364823500_ref22","doi-asserted-by":"crossref","first-page":"btad693","DOI":"10.1093\/bioinformatics\/btad693","article-title":"Molecular generation strategy and optimization based on A2C reinforcement learning in de novo drug design[J]","volume":"39","author":"Wang","year":"2023","journal-title":"Bioinformatics"},{"key":"2024091101364823500_ref23","doi-asserted-by":"crossref","first-page":"106585","DOI":"10.1016\/j.knosys.2020.106585","article-title":"SNF\u2013CVAE: computational method to predict drug\u2013disease interactions using similarity network fusion and collective variational autoencoder[J]","volume":"212","author":"Jarada","year":"2021","journal-title":"Knowl-Based Syst"},{"key":"2024091101364823500_ref24","doi-asserted-by":"crossref","first-page":"3250","DOI":"10.3390\/molecules25143250","article-title":"Relevant applications of generative adversarial networks in drug design and discovery: molecular de novo design, dimensionality reduction, and de novo peptide and protein design[J]","volume":"25","author":"Lin","year":"2020","journal-title":"Molecules"},{"key":"2024091101364823500_ref25","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s42979-023-02203-3","article-title":"PandoraGAN: generating antiviral peptides using generative adversarial network[J]","volume":"4","author":"Surana","year":"2023","journal-title":"SN Comput Sci"},{"key":"2024091101364823500_ref26","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/3307339.3342183","article-title":"GANDALF: a prototype of a GAN-based peptide design method[C]","volume-title":"Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","author":"Rossetto","year":"2019"},{"key":"2024091101364823500_ref27","doi-asserted-by":"crossref","first-page":"565644","DOI":"10.3389\/fphar.2020.565644","article-title":"Molecular sets (MOSES): a benchmarking platform for molecular generation models","volume":"11","author":"Polykovskiy","year":"2020","journal-title":"Front Pharmacol"},{"key":"2024091101364823500_ref28","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1021\/acs.jcim.8b00839","article-title":"GuacaMol: benchmarking models for de novo molecular design","volume":"59","author":"Brown","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2024091101364823500_ref29","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/978-1-60761-444-9_19","article-title":"The PeptideAtlas project","volume":"604","author":"Deutsch","year":"2010","journal-title":"Methods Mol Biol"},{"key":"2024091101364823500_ref30","doi-asserted-by":"crossref","DOI":"10.1107\/97809553602060000864","article-title":"PrimeX and the Schr\u00f6dinger computational chemistry suite of programs[J]","volume-title":"International Tables for Crystallography","author":"Bell","year":"2012"},{"key":"2024091101364823500_ref31","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2024091101364823500_ref32","doi-asserted-by":"crossref","first-page":"W5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties","volume":"49","author":"Xiong","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2024091101364823500_ref33","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","article-title":"Generating focused molecule libraries for drug discovery with recurrent neural networks","volume":"4","author":"Segler","year":"2018","journal-title":"ACS Cent Sci"},{"key":"2024091101364823500_ref34","doi-asserted-by":"crossref","first-page":"4398","DOI":"10.1021\/acs.molpharmaceut.8b00839","article-title":"Entangled conditional adversarial autoencoder for de novo drug discovery","volume":"15","author":"Polykovskiy","year":"2018","journal-title":"Mol Pharm"},{"key":"2024091101364823500_ref35","first-page":"2323","article-title":"Junction tree variational autoencoder for molecular graph generation","volume-title":"Proceedings of the 35th International Conference on Machine Learning","author":"Jin","year":"2018"},{"key":"2024091101364823500_ref36","first-page":"74","article-title":"A de novo molecular generation method using latent vector based generative adversarial network","volume":"11","author":"Prykhodko","year":"2019","journal-title":"Chemistry"},{"key":"2024091101364823500_ref37","article-title":"Objective-reinforced generative adversarial networks (organ) for sequence generation models","author":"Guimaraes","year":"2017"},{"key":"2024091101364823500_ref38","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1145\/3394486.3403104","article-title":"Moflow: an invertible flow model for generating molecular graphs[C]","volume-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Zang","year":"2020"},{"key":"2024091101364823500_ref39","doi-asserted-by":"crossref","first-page":"7","DOI":"10.12688\/f1000research.13446.1","article-title":"Immune regulation by Tim-3[J]","volume":"7","author":"Banerjee","year":"2018","journal-title":"F1000Research"},{"key":"2024091101364823500_ref40","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1021\/jm049081q","article-title":"Validation and useof the MM-PBSA approach for drug discovery","volume":"48","author":"Kuhn","year":"2005","journal-title":"Med Chem"},{"key":"2024091101364823500_ref41","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-84858-7_15","article-title":"Random forests","author":"Hastie","year":"2009","journal-title":"The Elements of Statistical Learning"},{"key":"2024091101364823500_ref42","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D convolutional neural networks for human action recognition[J]","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/5\/bbae444\/59073978\/bbae444.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/5\/bbae444\/59073978\/bbae444.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T08:33:22Z","timestamp":1732696402000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae444\/7754450"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,25]]},"references-count":42,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,7,25]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae444","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,9]]},"published":{"date-parts":[[2024,7,25]]},"article-number":"bbae444"}}