{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T18:08:49Z","timestamp":1789063729633,"version":"build-2803163510"},"reference-count":68,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Municipal Government of Quzhou","award":["2024D001"],"award-info":[{"award-number":["2024D001"]}]},{"DOI":"10.13039\/501100017649","name":"Hong Kong Government","doi-asserted-by":"publisher","award":["GHP\/231\/22GD"],"award-info":[{"award-number":["GHP\/231\/22GD"]}],"id":[{"id":"10.13039\/501100017649","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Gene expression profiles capture system-level drug responses and offer a promising basis for\n                    <jats:italic>de novo<\/jats:italic>\n                    molecular generation. However, their application is limited by data sparsity and experimental noise, which hinder the reliable mapping between disease-associated transcriptomic perturbations and chemically valid therapeutic molecules. Here, we present AET5, a\n                    <jats:italic>de novo<\/jats:italic>\n                    molecular generation framework that conditions molecular design on disease-reversal gene expression profiles. AET5 integrates contrastive self-supervised learning with pre-trained sequence-to-sequence models to learn robust associations between transcriptomic signatures and molecular structures by deriving noise-tolerant transcriptomic representations and aligning them with molecular sequence space. Across the L1000 dataset, AET5 outperforms existing expression-guided generation methods in generation quality and distributional characteristics, while maintaining favorable physicochemical and drug-related properties. We further apply AET5 to generate candidate compounds for SARS-CoV-2 infection and prostate cancer. Molecular docking and dynamics simulations indicate stable target binding, supporting the biological relevance of the generated molecules. These results demonstrate that disease-reversal expression profiles can effectively guide\n                    <jats:italic>de novo<\/jats:italic>\n                    molecular generation, providing a general framework for biologically informed drug design under noisy transcriptomic conditions.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014703","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T17:40:40Z","timestamp":1789062040000},"page":"e1014703","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["AET5: A transcriptome-guided molecular generation framework with contrastive self-supervised learning"],"prefix":"10.1371","volume":"22","author":[{"given":"Zhikang","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Suzhou University of Science and Technology","place":["Suzhou, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8868-8350","authenticated-orcid":true,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hangzhou Institute of Medicine, Chinese Academy of Sciences","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaoming","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":true,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China","place":["Quzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9207-1065","authenticated-orcid":true,"given":"Subhashisa","family":"Swain","sequence":"additional","affiliation":[{"name":"University of Oxford","place":["Oxford, Oxfordshire, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2911-7643","authenticated-orcid":true,"given":"Yijie","family":"Ding","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology","place":["Harbin, Heilongjiang, China"],"department":["Faculty of Computing"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prayag","family":"Tiwari","sequence":"additional","affiliation":[{"name":"School of Information Technology, Halmstad University","place":["Halmstad, Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuofeng","family":"Yuan","sequence":"additional","affiliation":[{"name":"The University of Hong Kong","place":["Hong Kong SAR, China"],"department":["Li Ka Shing Faculty of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyi","family":"Guo","sequence":"additional","affiliation":[{"name":"The University of Hong Kong","place":["Hong Kong SAR, China"],"department":["Li Ka Shing Faculty of Medicine"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"issue":"7019","key":"pcbi.1014703.ref001","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1038\/nature03192","article-title":"Chemical space and biology","volume":"432","author":"CM Dobson","year":"2004","journal-title":"Nature"},{"issue":"3","key":"pcbi.1014703.ref002","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1021\/ar500432k","article-title":"The chemical space project","volume":"48","author":"J-L Reymond","year":"2015","journal-title":"Acc Chem Res"},{"issue":"4","key":"pcbi.1014703.ref003","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":"RP Hertzberg","year":"2000","journal-title":"Curr Opin Chem Biol"},{"issue":"12","key":"pcbi.1014703.ref004","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1038\/s41573-022-00552-x","article-title":"Predictive validity in drug discovery: what it is, why it matters and how to improve it","volume":"21","author":"JW Scannell","year":"2022","journal-title":"Nat Rev Drug Discov"},{"issue":"1","key":"pcbi.1014703.ref005","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.healthpol.2010.12.002","article-title":"The cost of drug development: a systematic review","volume":"100","author":"S Morgan","year":"2011","journal-title":"Health Policy"},{"key":"pcbi.1014703.ref006","first-page":"1","article-title":"Artificial intelligence in drug discovery and development","volume-title":"Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays","author":"KK Mak","year":"2022"},{"issue":"2","key":"pcbi.1014703.ref007","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent Sci"},{"key":"pcbi.1014703.ref008","unstructured":"Kusner MJ, Paige B, Hern\u00e1ndez-Lobato JM. Grammar variational autoencoder. In: 2017."},{"key":"pcbi.1014703.ref009","unstructured":"Jin W, Barzilay R, Jaakkola T. Junction tree variational autoencoder for molecular graph generation. In: International Conference on Machine Learning. 2018."},{"issue":"51","key":"pcbi.1014703.ref010","doi-asserted-by":"crossref","first-page":"36048","DOI":"10.1039\/D3RA03954D","article-title":"Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets","volume":"13","author":"R Aphikulvanich","year":"2023","journal-title":"RSC Adv"},{"key":"pcbi.1014703.ref011","doi-asserted-by":"crossref","unstructured":"Li C, Yamanaka C, Kaitoh K, Yamanishi Y. Transformer-based Objective-reinforced Generative Adversarial Network to Generate Desired Molecules. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022. 3884\u201390. https:\/\/doi.org\/10.24963\/ijcai.2022\/539","DOI":"10.24963\/ijcai.2022\/539"},{"issue":"1","key":"pcbi.1014703.ref012","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1186\/s13321-019-0393-0","article-title":"Randomized SMILES strings improve the quality of molecular generative models","volume":"11","author":"J Ar\u00fas-Pous","year":"2019","journal-title":"J Cheminform"},{"issue":"3","key":"pcbi.1014703.ref013","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1021\/acs.jcim.9b00943","article-title":"Bidirectional molecule generation with recurrent neural networks","volume":"60","author":"F Grisoni","year":"2020","journal-title":"J Chem Inf Model"},{"key":"pcbi.1014703.ref014","unstructured":"Shi C, Xu M, Zhu Z, Zhang W, Zhang M, Tang J. GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. In: International Conference on Learning Representations. 2019."},{"key":"pcbi.1014703.ref015","doi-asserted-by":"crossref","unstructured":"Edwards C, Lai T, Ros K, Honke G, Cho K, Ji H. Translation between molecules and natural language. In: EMNLP 2022, Abu Dhabi, United Arab Emirates, 2022.","DOI":"10.18653\/v1\/2022.emnlp-main.26"},{"issue":"140","key":"pcbi.1014703.ref016","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"C Raffel","year":"2020","journal-title":"Journal of Machine Learning Research"},{"issue":"9","key":"pcbi.1014703.ref017","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1021\/acs.jcim.1c00600","article-title":"MolGPT: molecular generation using a transformer-decoder model","volume":"62","author":"V Bagal","year":"2022","journal-title":"J Chem Inf Model"},{"issue":"1","key":"pcbi.1014703.ref018","first-page":"015022","article-title":"Chemformer: a pre-trained transformer for computational chemistry","volume":"3","author":"R Irwin","year":"2022","journal-title":"Mach Learn: Sci Technol"},{"issue":"1","key":"pcbi.1014703.ref019","doi-asserted-by":"crossref","first-page":"55","DOI":"10.2174\/157489306775330615","article-title":"Gene Expression profile classification: a review","volume":"1","author":"MH Asyali","year":"2006","journal-title":"Current Bioinformatics"},{"issue":"1","key":"pcbi.1014703.ref020","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1038\/s41467-019-13807-w","article-title":"De novo generation of hit-like molecules from gene expression signatures using artificial intelligence","volume":"11","author":"O M\u00e9ndez-Lucio","year":"2020","journal-title":"Nat Commun"},{"key":"pcbi.1014703.ref021","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2020.00269","article-title":"Molecular generation for desired transcriptome changes with adversarial autoencoders","volume":"11","author":"R Shayakhmetov","year":"2020","journal-title":"Frontiers in Pharmacology"},{"key":"pcbi.1014703.ref022","article-title":"FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery","author":"TH Pham","year":"2022","journal-title":"bioRxiv"},{"issue":"9","key":"pcbi.1014703.ref023","doi-asserted-by":"crossref","first-page":"4303","DOI":"10.1021\/acs.jcim.1c00967","article-title":"TRIOMPHE: Transcriptome-based inference and generation of molecules with desired phenotypes by machine learning","volume":"61","author":"K Kaitoh","year":"2021","journal-title":"J Chem Inf Model"},{"issue":"5","key":"pcbi.1014703.ref024","article-title":"TransGEM: a molecule generation model based on Transformer with gene expression data","volume":"40","author":"Y Liu","year":"2024","journal-title":"Bioinformatics"},{"key":"pcbi.1014703.ref025","doi-asserted-by":"crossref","unstructured":"Li C, Yamanishi Y. De novo generation of hit-like molecules from gene expression profiles via deep learning. 2025.","DOI":"10.1007\/978-3-032-06066-2_20"},{"key":"pcbi.1014703.ref026","doi-asserted-by":"crossref","unstructured":"Li C, Yamanishi Y. GxVAEs: Two joint VAEs generate hit molecules from gene expression profiles. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2024. 13455\u201363.","DOI":"10.1609\/aaai.v38i12.29248"},{"key":"pcbi.1014703.ref027","doi-asserted-by":"crossref","unstructured":"Liu H, Tian S, Liu X. Phenotypic profile-informed generation of drug-like molecules via dual-channel variational autoencoders. In: International Joint Conferences on Artificial Intelligence Organization. 2025.","DOI":"10.24963\/ijcai.2025\/1008"},{"key":"pcbi.1014703.ref028","article-title":"A guide for bioinformaticians: \u2019omics-based drug discovery for precision oncology","author":"J Mun","year":"2020","journal-title":"Drug Discov Today"},{"issue":"3","key":"pcbi.1014703.ref029","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1038\/s42256-020-00285-9","article-title":"A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing","volume":"3","author":"T-H Pham","year":"2021","journal-title":"Nat Mach Intell"},{"key":"pcbi.1014703.ref030","doi-asserted-by":"crossref","first-page":"108581","DOI":"10.1016\/j.patcog.2022.108581","article-title":"Structure-aware conditional variational auto-encoder for constrained molecule optimization","volume":"126","author":"J Yu","year":"2022","journal-title":"Pattern Recognition"},{"key":"pcbi.1014703.ref031","doi-asserted-by":"crossref","first-page":"112114","DOI":"10.1016\/j.patcog.2025.112114","article-title":"Multilevel cross-domain relational network for drug repositioning","volume":"171","author":"D Niu","year":"2026","journal-title":"Pattern Recognition"},{"key":"pcbi.1014703.ref032","doi-asserted-by":"crossref","first-page":"111701","DOI":"10.1016\/j.patcog.2025.111701","article-title":"Knowledge-enhanced and structure-enhanced representation learning for protein\u2013ligand binding affinity prediction","volume":"166","author":"M Li","year":"2025","journal-title":"Pattern Recognition"},{"key":"pcbi.1014703.ref033","doi-asserted-by":"crossref","first-page":"112563","DOI":"10.1016\/j.patcog.2025.112563","article-title":"Protein interaction pattern recognition using heterogeneous semantics mining and hierarchical graph representation","volume":"172","author":"X Bi","year":"2026","journal-title":"Pattern Recognition"},{"key":"pcbi.1014703.ref034","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.3389\/fphar.2020.00269","article-title":"Erratum: addendum: molecular generation for desired transcriptome changes with adversarial autoencoders","volume":"11","author":"R Shayakhmetov","year":"2020","journal-title":"Front Pharmacol"},{"issue":"7","key":"pcbi.1014703.ref035","article-title":"From intention to implementation: automating biomedical research via LLMs","volume":"68","author":"Y Luo","year":"2025","journal-title":"Sci China Inf Sci"},{"issue":"2","key":"pcbi.1014703.ref036","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","article-title":"A learning algorithm for continually running fully recurrent neural networks","volume":"1","author":"RJ Williams","year":"1989","journal-title":"Neural Computation"},{"key":"pcbi.1014703.ref037","unstructured":"Oord A, Li Y, Vinyals O. Representation learning with contrastive predictive coding. 2019."},{"key":"pcbi.1014703.ref038","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning. 2020."},{"issue":"6","key":"pcbi.1014703.ref039","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2017.10.049","article-title":"A next generation connectivity map: L1000 platform and the first 1,000,000 profiles","volume":"171","author":"A Subramanian","year":"2017","journal-title":"Cell"},{"issue":"6","key":"pcbi.1014703.ref040","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.chom.2020.04.017","article-title":"Heightened innate immune responses in the respiratory tract of COVID-19 patients","volume":"27","author":"Z Zhou","year":"2020","journal-title":"Cell Host & Microbe"},{"issue":"5","key":"pcbi.1014703.ref041","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2020.04.026","article-title":"Imbalanced host response to SARS-CoV-2 drives development of COVID-19","volume":"181","author":"D Blanco-Melo","year":"2020","journal-title":"Cell"},{"key":"pcbi.1014703.ref042","article-title":"The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge","volume":"19","author":"K Tomczak","year":"2015","journal-title":"Contemp Oncol (Pozn)"},{"issue":"4","key":"pcbi.1014703.ref043","first-page":"045024","article-title":"Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation","volume":"1","author":"M Krenn","year":"2020","journal-title":"Mach Learn: Sci Technol"},{"key":"pcbi.1014703.ref044","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014."},{"key":"pcbi.1014703.ref045","unstructured":"Kingma DP, Welling M. Auto-Encoding Variational Bayes. 2022."},{"key":"pcbi.1014703.ref046","volume-title":"RDKit: Open-source cheminformatics","author":"G Landrum","year":"2014"},{"issue":"9","key":"pcbi.1014703.ref047","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1021\/acs.jcim.8b00234","article-title":"Fr\u00e9chet chemnet distance: a metric for generative models for molecules in drug discovery","volume":"58","author":"K Preuer","year":"2018","journal-title":"J Chem Inf Model"},{"key":"pcbi.1014703.ref048","doi-asserted-by":"crossref","first-page":"42717","DOI":"10.1038\/srep42717","article-title":"SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules","volume":"7","author":"A Daina","year":"2017","journal-title":"Sci Rep"},{"issue":"1","key":"pcbi.1014703.ref049","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1758-2946-1-8","article-title":"Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions","volume":"1","author":"P Ertl","year":"2009","journal-title":"J Cheminform"},{"issue":"2","key":"pcbi.1014703.ref050","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1038\/nchem.1243","article-title":"Quantifying the chemical beauty of drugs","volume":"4","author":"GR Bickerton","year":"2012","journal-title":"Nat Chem"},{"issue":"1","key":"pcbi.1014703.ref051","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1021\/ci700286x","article-title":"Natural product-likeness score and its application for prioritization of compound libraries","volume":"48","author":"P Ertl","year":"2008","journal-title":"J Chem Inf Model"},{"issue":"11","key":"pcbi.1014703.ref052","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1002\/cmdc.201600182","article-title":"A BOILED-Egg to predict gastrointestinal absorption and brain penetration of small molecules","volume":"11","author":"A Daina","year":"2016","journal-title":"ChemMedChem"},{"key":"pcbi.1014703.ref053","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gkaa1027","article-title":"Open Targets Platform: supporting systematic drug-target identification and prioritisation","volume":"49","author":"D Ochoa","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"pcbi.1014703.ref054","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1086\/346094","article-title":"Mutations in CHEK2 associated with prostate cancer risk","volume":"72","author":"X Dong","year":"2003","journal-title":"Am J Hum Genet"},{"key":"pcbi.1014703.ref055","doi-asserted-by":"crossref","first-page":"566579","DOI":"10.3389\/fgene.2020.566579","article-title":"Bioinformatics Analysis Discovers Microtubular Tubulin Beta 6 Class V (TUBB6) as a Potential Therapeutic Target in Glioblastoma","volume":"11","author":"L Jiang","year":"2020","journal-title":"Front Genet"},{"issue":"5","key":"pcbi.1014703.ref056","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.3390\/cells9051267","article-title":"A structural view of SARS-CoV-2 RNA replication machinery: RNA synthesis, proofreading and final capping","volume":"9","author":"M Romano","year":"2020","journal-title":"Cells"},{"issue":"1","key":"pcbi.1014703.ref057","doi-asserted-by":"crossref","first-page":"107690","DOI":"10.1016\/j.jsb.2020.107690","article-title":"Structural insights of key enzymes into therapeutic intervention against SARS-CoV-2","volume":"213","author":"M Shahid","year":"2021","journal-title":"J Struct Biol"},{"issue":"2","key":"pcbi.1014703.ref058","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading","volume":"31","author":"O Trott","year":"2010","journal-title":"J Comput Chem"},{"issue":"20","key":"pcbi.1014703.ref059","doi-asserted-by":"crossref","first-page":"6183","DOI":"10.1021\/acs.jcim.3c01153","article-title":"AmberTools","volume":"63","author":"DA Case","year":"2023","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"1","key":"pcbi.1014703.ref060","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1109\/JBHI.2025.3585290","article-title":"LLM-DDI: leveraging large language models for drug-drug interaction prediction on biomedical knowledge graph","volume":"30","author":"D Li","year":"2026","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"pcbi.1014703.ref061","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/JBHI.2025.3600045","article-title":"Multi-view contrastive learning for drug-drug interaction event prediction","volume":"30","author":"D Li","year":"2026","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"pcbi.1014703.ref062","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1038\/s41551-024-01312-5","article-title":"Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning","volume":"9","author":"X Su","year":"2025","journal-title":"Nat Biomed Eng"},{"issue":"41","key":"pcbi.1014703.ref063","doi-asserted-by":"crossref","DOI":"10.4028\/b-XDTcg5","article-title":"Controllable generation of pathogen-specific antimicrobial peptides through knowledge-aware prompt diffusion model","volume":"12","author":"Y Wang","year":"2025","journal-title":"Adv Sci (Weinh)"},{"issue":"1","key":"pcbi.1014703.ref064","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.1038\/s41467-026-69302-6","article-title":"Multi-to-uni modal knowledge transfer pre-training for molecular representation learning","volume":"17","author":"Z Xiong","year":"2026","journal-title":"Nat Commun"},{"issue":"4","key":"pcbi.1014703.ref065","article-title":"Computational approaches for circRNA-disease association prediction: a review","volume":"19","author":"M Niu","year":"2024","journal-title":"Front Comput Sci"},{"issue":"1","key":"pcbi.1014703.ref066","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41589-025-02110-z","article-title":"Harnessing advances in artificial intelligence for protein design","volume":"22","author":"R Johnson","year":"2026","journal-title":"Nat Chem Biol"},{"issue":"2","key":"pcbi.1014703.ref067","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1038\/s41587-023-01987-2","article-title":"Computational drug development for membrane protein targets","volume":"42","author":"H Li","year":"2024","journal-title":"Nat Biotechnol"},{"issue":"11","key":"pcbi.1014703.ref068","doi-asserted-by":"crossref","DOI":"10.1007\/s11432-024-4171-9","article-title":"SBSM-Pro: support bio-sequence machine for proteins","volume":"67","author":"Y Wang","year":"2024","journal-title":"Sci China Inf Sci"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014703","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T17:40:51Z","timestamp":1789062051000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014703"}},"subtitle":[],"editor":[{"given":"Fei","family":"Guo","sequence":"first","affiliation":[{"name":"Central South University","place":["CHINA"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":68,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,10]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1014703","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]}}}