{"id":"W4413258170","doi":"10.1038/s41565-025-01975-4","title":"Designing lipid nanoparticles using a transformer-based neural network","year":2025,"lang":"en","type":"article","venue":"Nature Nanotechnology","topic":"Lipid Membrane Structure and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology; Ministry of Education - Singapore; Massachusetts Institute of Technology; Nanyang Technological University; Brigham and Women's Hospital; National Cancer Institute; Advanced Research Projects Agency; National Science Foundation; Advanced Research Projects Agency for Health","keywords":"Artificial neural network; Transformer; Computer science; Nanoparticle; Deep learning; Artificial intelligence; Nanotechnology; Chemistry; Biological system; Materials science; Engineering; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002338991,0.0004052785,0.0002782819,0.0001766619,0.0001438353,0.0003067752,0.0005535687,0.0006289599,0.001058589],"category_scores_gemma":[0.0003970587,0.000224064,0.0003140802,0.0001478892,0.0003050021,0.000481286,0.0003220402,0.0004444568,0.0002805625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006811941,"about_ca_system_score_gemma":0.0005361504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002640197,"about_ca_topic_score_gemma":0.003729429,"domain_scores_codex":[0.9999378,0.0000101585,0.000002510029,0.00001819078,0.0000195266,0.00001188104],"domain_scores_gemma":[0.9999311,0.00002838628,0.00001058581,0.000004389495,0.00001877715,0.000006737671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000819079,0.00009927022,0.0008286969,0.00008295571,0.00002609894,0.00008732705,0.00002184832,0.9109723,0.04645264,0.004392069,0.0007017595,0.0362532],"study_design_scores_gemma":[0.000003692942,0.00002386719,0.0000265158,0.000001623543,0.000003007539,0.000005762011,0.000002102285,0.9959754,0.003368353,0.0004051144,0.0001826001,0.000001964258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1859735,0.0005598684,0.7994176,0.0005943966,0.00009575807,0.0001278069,0.0001407846,0.0009497366,0.01214059],"genre_scores_gemma":[0.8800862,0.0002951046,0.1145423,0.0002695581,0.00001168441,0.0001540654,0.0001104379,0.00004716587,0.004483557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002640197,"threshold_uncertainty_score":0.005249619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007641857642806685,"score_gpt":0.2629388419037921,"score_spread":0.2552969842609855,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}