{"doi":"10.1101/2025.06.17.659751","title":"A Unified Framework for Model-Informed and Agentic RNA Design","abstract":"ABSTRACT End-to-end, machine-learning-based design of mRNA molecules offers a powerful means to tailor their properties for specific tasks. mRNA expression level, immunogenicity, tissue specificity, stability, and localization strongly depend on sequence, providing a rich set of properties amenable to optimization. Despite this potential, the components of mRNA are governed by distinct grammatical and functional rules that hinder a unified approach to complete mRNA design. While machine learning and generative AI techniques can excel on individual sequence design tasks, out-of-distribution design, where the biological objective shifts substantially from the original training data, remains difficult. Moreover, there is a disconnect between available sequence generation technologies and the diverse body of biological datasets needed to form and test mechanistic hypotheses. In this work, we describe a simple and powerful alteration to integrated gradients (Design by I ntegrated G radients or DIGs) that serves as the foundation for several mRNA design tasks and an agentic hypothesis engine, the St ructured R NA E vidence A ggregation M odule (STREAM), which enables rapid adaptation of this technique to new contexts. Using this framework, we demonstrate complete model-informed mRNA design and reveal the underexplored rules governing the assembly of mRNA components into high-performance transcripts. By linking neural-network-based design to independent datasets, we design complete mRNA sequences in shifted settings, culminating in up to 6-fold increases in intramuscular expression compared to state-of-the-art methods in vivo. Together, DIGs and STREAM enable automated mRNA design in increasingly complex settings.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":556459,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9488,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1455694,"name":"McKayla Vlasity","orcid":"0000-0002-3749-9437","position":1,"is_corresponding":false},{"id":1455695,"name":"Wyatt M. Becicka","orcid":"0009-0008-6692-2741","position":2,"is_corresponding":false},{"id":1455696,"name":"Juanjuan Huang","orcid":"0000-0002-6608-947X","position":3,"is_corresponding":false},{"id":1456126,"name":"Sicheng Pang","orcid":null,"position":4,"is_corresponding":false},{"id":495247,"name":"Wilson W. Wong","orcid":"0000-0001-8394-889X","position":5,"is_corresponding":false},{"id":290956,"name":"Mark W. Grinstaff","orcid":"0000-0002-5453-3668","position":6,"is_corresponding":false},{"id":275750,"name":"Alexander A. Green","orcid":"0000-0003-2058-1204","position":7,"is_corresponding":false},{"id":1162575,"name":"Aidan T. Riley","orcid":"0000-0001-7867-2585","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-19T02:55:08.896385Z","pmid":"40667082","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}