{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:42:13Z","timestamp":1784544133034,"version":"3.55.0"},"reference-count":113,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82273855"],"award-info":[{"award-number":["82273855"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2225002"],"award-info":[{"award-number":["T2225002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82204278"],"award-info":[{"award-number":["82204278"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M763421"],"award-info":[{"award-number":["2024M763421"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC2305904"],"award-info":[{"award-number":["2023YFC2305904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3400504"],"award-info":[{"award-number":["2022YFC3400504"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002367","name":"Chinese Academy of Sciences","doi-asserted-by":"publisher","award":["XDB0830000"],"award-info":[{"award-number":["XDB0830000"]}],"id":[{"id":"10.13039\/501100002367","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015796","name":"Shanghai Lingjun Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015796","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Acta Pharmaceutica Sinica B"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1016\/j.apsb.2025.10.034","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:25:51Z","timestamp":1761697551000},"page":"122-136","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Omics-based large language models: A new engine for drug discovery innovation"],"prefix":"10.1016","volume":"16","author":[{"given":"Xia","family":"Sheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoya","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqi","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanlong","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochu","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9547-0643","authenticated-orcid":false,"given":"Xutong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.apsb.2025.10.034_bib1","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1038\/nrd3480","article-title":"How were new medicines discovered?","volume":"10","author":"Swinney","year":"2011","journal-title":"Nat Rev Drug Discov"},{"key":"10.1016\/j.apsb.2025.10.034_bib2","doi-asserted-by":"crossref","first-page":"12651","DOI":"10.1021\/acs.jmedchem.2c01737","article-title":"Is target-based drug discovery efficient? Discovery and \u201coff-target\u201d mechanisms of all drugs","volume":"66","author":"Sadri","year":"2023","journal-title":"J Med Chem"},{"key":"10.1016\/j.apsb.2025.10.034_bib3","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1038\/s41573-022-00472-w","article-title":"Phenotypic drug discovery: recent successes, lessons learned and new directions","volume":"21","author":"Vincent","year":"2022","journal-title":"Nat Rev Drug Discov"},{"key":"10.1016\/j.apsb.2025.10.034_bib4","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/nmeth1156","article-title":"Next-generation sequencing transforms today's biology","volume":"5","author":"Schuster","year":"2008","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib5","doi-asserted-by":"crossref","first-page":"5339","DOI":"10.1109\/JBHI.2025.3550353","article-title":"ExplainMIX: explaining drug response prediction in directed graph neural networks with multi-omics fusion","volume":"29","author":"Xiang","year":"2025","journal-title":"IEEE J Biomed Health Inform"},{"key":"10.1016\/j.apsb.2025.10.034_bib6","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/nrg.2018.4","article-title":"Integrative omics for health and disease","volume":"19","author":"Karczewski","year":"2018","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.apsb.2025.10.034_bib7","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1038\/s41582-020-0350-6","article-title":"Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke","volume":"16","author":"Montaner","year":"2020","journal-title":"Nat Rev Neurol"},{"key":"10.1016\/j.apsb.2025.10.034_bib8","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1038\/s41592-023-02144-y","article-title":"scPerturb: harmonized single-cell perturbation data","volume":"21","author":"Peidli","year":"2024","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib9","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1016\/j.csbj.2024.04.058","article-title":"A mini-review on perturbation modelling across single-cell omic modalities","volume":"23","author":"Gavriilidis","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"key":"10.1016\/j.apsb.2025.10.034_bib10","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":"Subramanian","year":"2017","journal-title":"Cell"},{"key":"10.1016\/j.apsb.2025.10.034_bib11","doi-asserted-by":"crossref","first-page":"D1099","DOI":"10.1093\/nar\/gkae858","article-title":"PerturBase: a comprehensive database for single-cell perturbation data analysis and visualization","volume":"53","author":"Wei","year":"2024","journal-title":"Nucleic Acids Res"},{"key":"10.1016\/j.apsb.2025.10.034_bib12","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"L\u00e4hnemann","year":"2020","journal-title":"Genome Biol"},{"key":"10.1016\/j.apsb.2025.10.034_bib13","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1038\/s41467-021-27729-z","article-title":"Zero-preserving imputation of single-cell RNA-seq data","volume":"13","author":"Linderman","year":"2022","journal-title":"Nat Commun"},{"key":"10.1016\/j.apsb.2025.10.034_bib14","doi-asserted-by":"crossref","first-page":"7371","DOI":"10.3390\/app15137371","article-title":"Data imputation based on retrieval-augmented generation","volume":"15","author":"Shi","year":"2025","journal-title":"Appl Sci"},{"key":"10.1016\/j.apsb.2025.10.034_bib15","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"10.1016\/j.apsb.2025.10.034_bib16","article-title":"N-gram language modeling using recurrent neural network estimation","author":"Chelba","year":"2017","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib17","article-title":"Recurrent neural networks (RNNs): a gentle introduction and overview","author":"Schmidt","year":"2019","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129190","article-title":"Understanding LLMs: a comprehensive overview from training to inference","volume":"620","author":"Liu","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.apsb.2025.10.034_bib19","article-title":"Between words and characters: a brief history of open-vocabulary modeling and tokenization in NLP","author":"Mielke","year":"2021","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib20","article-title":"Attention is all you need","author":"Vaswani","year":"2023","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib21","article-title":"Language models are few-shot learners","author":"Brown","year":"2020","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib22","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAl blog"},{"key":"10.1016\/j.apsb.2025.10.034_bib23","first-page":"4171","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019","journal-title":"ACL Anthology"},{"key":"10.1016\/j.apsb.2025.10.034_bib24","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2023","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.apsb.2025.10.034_bib25","first-page":"2503.05788","article-title":"Emergent abilities in large language models: a survey","author":"Berti","year":"2025","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib26","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13073-017-0467-4","article-title":"A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications","volume":"9","author":"Haque","year":"2017","journal-title":"Genome Med"},{"key":"10.1016\/j.apsb.2025.10.034_bib27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nrg2484","article-title":"RNA-Seq: a revolutionary tool for transcriptomics","volume":"10","author":"Wang","year":"2009","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.apsb.2025.10.034_bib28","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1038\/nbt1010-1049","article-title":"Epigenomics reveals a functional genome anatomy and a new approach to common disease","volume":"28","author":"Feinberg","year":"2010","journal-title":"Nat Biotechnol"},{"key":"10.1016\/j.apsb.2025.10.034_bib29","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1128\/MCB.19.3.1720","article-title":"Correlation between protein and mRNA abundance in yeast","volume":"19","author":"Gygi","year":"1999","journal-title":"Mol Cell Biol"},{"key":"10.1016\/j.apsb.2025.10.034_bib30","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.cell.2012.09.019","article-title":"Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent Cells","volume":"151","author":"Marguerat","year":"2012","journal-title":"Cell"},{"key":"10.1016\/j.apsb.2025.10.034_bib31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41392-023-01380-0","article-title":"To metabolomics and beyond: a technological portfolio to investigate cancer metabolism","volume":"8","author":"Danzi","year":"2023","journal-title":"Signal Transduct Targeted Ther"},{"key":"10.1016\/j.apsb.2025.10.034_bib32","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/14737159.2017.1268533","article-title":"NanoString, a novel digital color-coded barcode technology: current and future applications in molecular diagnostics","volume":"17","author":"Tsang","year":"2017","journal-title":"Expert Rev Mol Diagn"},{"key":"10.1016\/j.apsb.2025.10.034_bib33","doi-asserted-by":"crossref","first-page":"5463","DOI":"10.1073\/pnas.74.12.5463","article-title":"DNA sequencing with chain-terminating inhibitors","volume":"74","author":"Sanger","year":"1977","journal-title":"Proc Natl Acad Sci USA"},{"key":"10.1016\/j.apsb.2025.10.034_bib34","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1126\/science.270.5235.467","article-title":"Quantitative monitoring of gene expression patterns with a complementary DNA microarray","volume":"270","author":"Schena","year":"1995","journal-title":"Science"},{"key":"10.1016\/j.apsb.2025.10.034_bib35","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1038\/nprot.2013.143","article-title":"Genome engineering using the CRISPR-Cas9 system","volume":"8","author":"Ran","year":"2013","journal-title":"Nat Protoc"},{"key":"10.1016\/j.apsb.2025.10.034_bib36","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1038\/nbt0492-413","article-title":"Simultaneous amplification and detection of specific DNA sequences","volume":"10","author":"Higuchi","year":"1992","journal-title":"Nat Biotechnol"},{"key":"10.1016\/j.apsb.2025.10.034_bib37","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1073\/pnas.63.2.378","article-title":"Formation and detection of RNA\u2013DNA hybrid molecules in cytological preparations","volume":"63","author":"Gall","year":"1969","journal-title":"Proc Natl Acad Sci USA"},{"key":"10.1016\/j.apsb.2025.10.034_bib38","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s13073-022-01075-1","article-title":"An introduction to spatial transcriptomics for biomedical research","volume":"14","author":"Williams","year":"2022","journal-title":"Genome Med"},{"key":"10.1016\/j.apsb.2025.10.034_bib39","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1093\/bioinformatics\/btab083","article-title":"DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome","volume":"37","author":"Ji","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.apsb.2025.10.034_bib40","article-title":"DNABERT-2: efficient foundation model and benchmark for multi-species genome","author":"Zhou","year":"2024","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib41","article-title":"Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions","author":"Chen","year":"2022","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib42","doi-asserted-by":"crossref","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":"10.1016\/j.apsb.2025.10.034_bib43","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1038\/s42256-022-00534-z","article-title":"scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data","volume":"4","author":"Yang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.apsb.2025.10.034_bib44","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.1038\/s41592-024-02201-0","article-title":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI","volume":"21","author":"Cui","year":"2024","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib45","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.apsb.2025.10.034_bib46","article-title":"CellPLM: pre-training of cell language model beyond single cells","author":"Wen","year":"2023","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib47","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nmeth.2930","article-title":"Validation of noise models for single-cell transcriptomics","volume":"11","author":"Gr\u00fcn","year":"2014","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib48","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41592-024-02214-9","article-title":"scPROTEIN: a versatile deep graph contrastive learning framework for single-cell proteomics embedding","volume":"21","author":"Li","year":"2024","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib49","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1038\/s41580-023-00615-w","article-title":"The technological landscape and applications of single-cell multi-omics","volume":"24","author":"Baysoy","year":"2023","journal-title":"Nat Rev Mol Cell Biol"},{"key":"10.1016\/j.apsb.2025.10.034_bib50","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41467-017-02554-5","article-title":"A general and flexible method for signal extraction from single-cell RNA-seq data","volume":"9","author":"Risso","year":"2018","journal-title":"Nat Commun"},{"key":"10.1016\/j.apsb.2025.10.034_bib51","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1186\/s13059-019-1874-1","article-title":"Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression","volume":"20","author":"Hafemeister","year":"2019","journal-title":"Genome Biol"},{"key":"10.1016\/j.apsb.2025.10.034_bib52","doi-asserted-by":"crossref","DOI":"10.3390\/ijms242115858","article-title":"Deep learning for genomics: from early neural nets to modern large language models","volume":"24","author":"Yue","year":"2023","journal-title":"Int J Mol Sci"},{"key":"10.1016\/j.apsb.2025.10.034_bib53","article-title":"To transformers and beyond: large language models for the genome","author":"Consens","year":"2023","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib54","article-title":"Large language models in bioinformatics: applications and perspectives","author":"Liu","year":"2024","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib55","article-title":"DNA language models are powerful predictors of genome-wide variant effects","author":"Benegas","year":"2022","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib56","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1093\/bib\/bbw114","article-title":"Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences","volume":"19","author":"Manzoni","year":"2018","journal-title":"Briefings Bioinf"},{"key":"10.1016\/j.apsb.2025.10.034_bib57","doi-asserted-by":"crossref","first-page":"57","DOI":"10.4331\/wjbc.v12.i5.57","article-title":"Proteomics: concepts and applications in human medicine","volume":"12","author":"Al-Amrani","year":"2021","journal-title":"World J Biol Chem"},{"key":"10.1016\/j.apsb.2025.10.034_bib58","article-title":"HyenaDNA: long-range genomic sequence modeling at single nucleotide resolution","author":"Nguyen","year":"2023","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib59","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1109\/TCBB.2023.3237769","article-title":"BERT2OME: prediction of 2\u2032-O-methylation modifications from RNA sequence by transformer architecture based on BERT","volume":"20","author":"Soylu","year":"2023","journal-title":"IEEE ACM Trans Comput Biol Bioinf"},{"key":"10.1016\/j.apsb.2025.10.034_bib60","article-title":"GenerRNA: a generative pre-trained language model for de novo RNA design","volume":"19","author":"Zhao","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.apsb.2025.10.034_bib61","article-title":"AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model","author":"Avsec","year":"2025","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib62","article-title":"ProLLM: protein chain-of-thoughts enhanced LLM for protein\u2013protein interaction prediction","volume":"2405","author":"Jin","year":"2024","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib63","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1038\/nbt.3300","article-title":"Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning","volume":"33","author":"Alipanahi","year":"2015","journal-title":"Nat Biotechnol"},{"key":"10.1016\/j.apsb.2025.10.034_bib64","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1126\/science.1105136","article-title":"The ENCODE (ENCyclopedia of DNA elements) project","volume":"306","author":"Feingold","year":"2004","journal-title":"Science"},{"key":"10.1016\/j.apsb.2025.10.034_bib65","article-title":"Caduceus: bi-directional equivariant long-range DNA sequence modeling","author":"Schiff","year":"2024","journal-title":"arXiv preprint"},{"key":"10.1016\/j.apsb.2025.10.034_bib66","doi-asserted-by":"crossref","DOI":"10.1038\/mtna.2016.37","article-title":"iRNA-PseU: identifying RNA pseudouridine sites","volume":"5","author":"Chen","year":"2016","journal-title":"Mol Ther Nucleic Acids"},{"key":"10.1016\/j.apsb.2025.10.034_bib67","series-title":"2016 short papers","first-page":"207","article-title":"Attention-based bidirectional long short-term memory networks for relation classification","author":"Zhou","year":"2016"},{"key":"10.1016\/j.apsb.2025.10.034_bib68","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1038\/nmeth.3547","article-title":"Predicting effects of noncoding variants with deep learning-based sequence model","volume":"12","author":"Zhou","year":"2015","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib69","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1126\/science.1262110","article-title":"The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans","volume":"348","author":"The","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.apsb.2025.10.034_bib70","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/nature13182","article-title":"A promoter-level mammalian expression atlas","volume":"507","year":"2014","journal-title":"Nature"},{"key":"10.1016\/j.apsb.2025.10.034_bib71","article-title":"ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants","author":"Pampari","year":"2024","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib72","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2018.12.015","article-title":"Predicting splicing from primary sequence with deep learning","volume":"176","author":"Jaganathan","year":"2019","journal-title":"Cell"},{"key":"10.1016\/j.apsb.2025.10.034_bib73","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1038\/s41588-022-01065-4","article-title":"Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale","volume":"54","author":"Zhou","year":"2022","journal-title":"Nat Genet"},{"key":"10.1016\/j.apsb.2025.10.034_bib74","doi-asserted-by":"crossref","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":"10.1016\/j.apsb.2025.10.034_bib75","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1038\/s41592-024-02305-7","article-title":"Large-scale foundation model on single-cell transcriptomics","volume":"21","author":"Hao","year":"2024","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib76","article-title":"Predicting cellular responses to perturbation across diverse contexts with STATE","author":"Adduri","year":"2025","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib77","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/s41573-023-00688-4","article-title":"Applications of single-cell RNA sequencing in drug discovery and development","volume":"22","author":"Van de Sande","year":"2023","journal-title":"Nat Rev Drug Discov"},{"key":"10.1016\/j.apsb.2025.10.034_bib78","doi-asserted-by":"crossref","DOI":"10.1126\/science.aba7721","article-title":"A human cell atlas of fetal gene expression","volume":"370","author":"Cao","year":"2020","journal-title":"Science"},{"key":"10.1016\/j.apsb.2025.10.034_bib79","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.1038\/s41467-025-58699-1","article-title":"scPRINT: pre-training on 50 million cells allows robust gene network predictions","volume":"16","author":"Kalfon","year":"2025","journal-title":"Nat Commun"},{"key":"10.1016\/j.apsb.2025.10.034_bib80","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1002\/path.5751","article-title":"Single-cell analysis of mouse and human prostate reveals novel fibroblasts with specialized distribution and microenvironment interactions","volume":"255","author":"Joseph","year":"2021","journal-title":"J Pathol"},{"key":"10.1016\/j.apsb.2025.10.034_bib81","article-title":"Cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices","author":"Megill","year":"2021","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib82","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1126\/science.aax4438","article-title":"Exploring genetic interaction manifolds constructed from rich single-cell phenotypes","volume":"365","author":"Norman","year":"2019","journal-title":"Science"},{"key":"10.1016\/j.apsb.2025.10.034_bib83","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1126\/science.1132939","article-title":"The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease","volume":"313","author":"Lamb","year":"2006","journal-title":"Science"},{"key":"10.1016\/j.apsb.2025.10.034_bib84","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s13238-021-00885-0","article-title":"Drug target inference by mining transcriptional data using a novel graph convolutional network framework","volume":"13","author":"Zhong","year":"2022","journal-title":"Protein Cell"},{"key":"10.1016\/j.apsb.2025.10.034_bib85","article-title":"Identifying compound\u2013protein interactions with knowledge graph embedding of perturbation transcriptomics","volume":"4","author":"Ni","year":"2024","journal-title":"Cell Genom"},{"key":"10.1016\/j.apsb.2025.10.034_bib86","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/s41551-024-01284-6","article-title":"Simple and effective embedding model for single-cell biology built from ChatGPT","volume":"9","author":"Chen","year":"2025","journal-title":"Nat Biomed Eng"},{"key":"10.1016\/j.apsb.2025.10.034_bib87","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s00018-014-1772-3","article-title":"Prediction of individual response to anticancer therapy: historical and future perspectives","volume":"72","author":"Unger","year":"2015","journal-title":"Cell Mol Life Sci"},{"key":"10.1016\/j.apsb.2025.10.034_bib88","doi-asserted-by":"crossref","first-page":"5680","DOI":"10.1038\/s41467-022-33291-z","article-title":"Gene expression based inference of cancer drug sensitivity","volume":"13","author":"Chawla","year":"2022","journal-title":"Nat Commun"},{"key":"10.1016\/j.apsb.2025.10.034_bib89","doi-asserted-by":"crossref","first-page":"5009","DOI":"10.3390\/cancers13195009","article-title":"Drug-induced resistance and phenotypic switch in triple-negative breast cancer can be controlled via resolution and targeting of individualized signaling signatures","volume":"13","author":"Vasudevan","year":"2021","journal-title":"Cancers"},{"key":"10.1016\/j.apsb.2025.10.034_bib90","article-title":"Enabling single-cell drug response annotations from bulk RNA-seq using SCAD","volume":"10","author":"Zheng","year":"2023","journal-title":"Adv Sci"},{"key":"10.1016\/j.apsb.2025.10.034_bib91","doi-asserted-by":"crossref","DOI":"10.1093\/bioadv\/vbae199","article-title":"A comparative analysis of gene expression profiling by statistical and machine learning approaches","volume":"5","author":"Bontonou","year":"2024","journal-title":"Bioinform Adv"},{"key":"10.1016\/j.apsb.2025.10.034_bib92","doi-asserted-by":"crossref","first-page":"5378","DOI":"10.1038\/s41467-024-49620-3","article-title":"Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery","volume":"15","author":"Tong","year":"2024","journal-title":"Nat Commun"},{"key":"10.1016\/j.apsb.2025.10.034_bib93","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1038\/s41587-021-00946-z","article-title":"Prediction of drug efficacy from transcriptional profiles with deep learning","volume":"39","author":"Zhu","year":"2021","journal-title":"Nat Biotechnol"},{"key":"10.1016\/j.apsb.2025.10.034_bib94","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":"Pham","year":"2021","journal-title":"Nat Mach Intell"},{"key":"10.1016\/j.apsb.2025.10.034_bib95","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1038\/s41422-024-01034-y","article-title":"GeneCompass: deciphering universal gene regulatory mechanisms with a knowledge-informed cross-species foundation model","volume":"34","author":"Yang","year":"2024","journal-title":"Cell Res"},{"key":"10.1016\/j.apsb.2025.10.034_bib96","doi-asserted-by":"crossref","first-page":"7045","DOI":"10.1016\/j.cell.2024.11.015","article-title":"How to build the virtual cell with artificial intelligence: priorities and opportunities","volume":"187","author":"Bunne","year":"2024","journal-title":"Cell"},{"key":"10.1016\/j.apsb.2025.10.034_bib97","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1016\/j.cell.2025.06.008","article-title":"Virtual cell challenge: toward a turing test for the virtual cell","volume":"188","author":"Roohani","year":"2025","journal-title":"Cell"},{"key":"10.1016\/j.apsb.2025.10.034_bib98","article-title":"Tahoe-100M: a giga-scale single-cell perturbation atlas for context-dependent gene function and cellular modeling","author":"Zhang","year":"2025","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib99","doi-asserted-by":"crossref","first-page":"6876","DOI":"10.1038\/s41467-021-27150-6","article-title":"scCODA is a Bayesian model for compositional single-cell data analysis","volume":"12","author":"B\u00fcttner","year":"2021","journal-title":"Nat Commun"},{"key":"10.1016\/j.apsb.2025.10.034_bib100","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1038\/s41592-024-02353-z","article-title":"Transformers in single-cell omics: a review and new perspectives","volume":"21","author":"Sza\u0142ata","year":"2024","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib101","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11427-023-2561-0","article-title":"Single-cell omics: experimental workflow, data analyses and applications","volume":"68","author":"Sun","year":"2025","journal-title":"Sci China Life Sci"},{"key":"10.1016\/j.apsb.2025.10.034_bib102","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/s41580-024-00768-2","article-title":"Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics","volume":"26","author":"Gulati","year":"2025","journal-title":"Nat Rev Mol Cell Biol"},{"key":"10.1016\/j.apsb.2025.10.034_bib103","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1038\/s41576-024-00738-6","article-title":"Next-generation data filtering in the genomics era","volume":"25","author":"Hemstrom","year":"2024","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.apsb.2025.10.034_bib104","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/s41592-021-01333-x","article-title":"Single-cell metabolomics hits its stride","volume":"18","author":"Seydel","year":"2021","journal-title":"Nat Methods"},{"key":"10.1016\/j.apsb.2025.10.034_bib105","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1038\/s41580-024-00756-6","article-title":"Harnessing the deep learning power of foundation models in single-cell omics","volume":"25","author":"Ma","year":"2024","journal-title":"Nat Rev Mol Cell Biol"},{"key":"10.1016\/j.apsb.2025.10.034_bib106","first-page":"782","article-title":"Transformer interpretability beyond attention visualization","author":"Chefer","year":"2021","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"10.1016\/j.apsb.2025.10.034_bib107","article-title":"Evaluating foundation models for in-silico perturbation","author":"Liu","year":"2025","journal-title":"bioRxiv"},{"key":"10.1016\/j.apsb.2025.10.034_bib108","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1111\/prd.12373","article-title":"Microbiomics: were we all wrong before?","volume":"85","author":"Kumar","year":"2000","journal-title":"Periodontol"},{"key":"10.1016\/j.apsb.2025.10.034_bib109","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1161\/CIRCRESAHA.118.310998","article-title":"Epigenomics: technologies and applications","volume":"122","author":"Wang","year":"2018","journal-title":"Circ Res"},{"key":"10.1016\/j.apsb.2025.10.034_bib110","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1146\/annurev-biodatasci-102523-103640","article-title":"Spatially resolved single-cell omics: methods, challenges, and future perspectives","volume":"7","author":"Dezem","year":"2024","journal-title":"Annu Rev Biomed Data Sci"},{"key":"10.1016\/j.apsb.2025.10.034_bib111","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41576-023-00580-2","article-title":"Methods and applications for single-cell and spatial multi-omics","volume":"24","author":"Vandereyken","year":"2023","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.apsb.2025.10.034_bib112","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1038\/s41576-023-00586-w","article-title":"Best practices for single-cell analysis across modalities","volume":"24","author":"Heumos","year":"2023","journal-title":"Nat Rev Genet"},{"key":"10.1016\/j.apsb.2025.10.034_bib113","article-title":"CELLama: foundation model for single cell and spatial transcriptomics by cell embedding leveraging language model abilities","author":"Choi","year":"2024","journal-title":"bioRxiv"}],"container-title":["Acta Pharmaceutica Sinica B"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2211383525007129?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2211383525007129?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:08:20Z","timestamp":1771520900000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2211383525007129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":113,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["S2211383525007129"],"URL":"https:\/\/doi.org\/10.1016\/j.apsb.2025.10.034","relation":{},"ISSN":["2211-3835"],"issn-type":[{"value":"2211-3835","type":"print"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Omics-based large language models: A new engine for drug discovery innovation","name":"articletitle","label":"Article Title"},{"value":"Acta Pharmaceutica Sinica B","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.apsb.2025.10.034","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V. on behalf of Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences.","name":"copyright","label":"Copyright"}]}}