{"id":"W4413257724","doi":"10.1016/j.cell.2025.07.033","title":"A generative deep learning approach to de novo antibiotic design","year":2025,"lang":"en","type":"article","venue":"Cell","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Audacious Project; National Science Foundation Graduate Research Fellowship Program; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; Defense Threat Reduction Agency; Knut och Alice Wallenbergs Stiftelse; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Flu Lab; Broad Institute; Siebel Scholars Foundation; Oracle; Division of Intramural Research, National Institute of Allergy and Infectious Diseases; Wyss Foundation; James S. McDonnell Foundation","keywords":"Biology; In silico; Antibiotics; Neisseria gonorrhoeae; Computational biology; Synthetic biology; Staphylococcus aureus; Generative Design; Antibacterial activity; Antimicrobial; Antibiotic resistance; Generative grammar; Generative model; Antimicrobial peptides; Microbiology; Artificial intelligence; Bacteria; Genetics; Computer science; Gene; Materials science","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":[0.0008850903,0.0004896874,0.0009737459,0.0008410445,0.0004817107,0.000966268,0.001954298,0.001644544,0.003789151],"category_scores_gemma":[0.002911843,0.0008066111,0.001079338,0.0008225964,0.001117832,0.0009091582,0.001598037,0.001865208,0.0005531557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001113649,"about_ca_system_score_gemma":0.001286582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005028459,"about_ca_topic_score_gemma":0.008978124,"domain_scores_codex":[0.9996811,0.0001176952,0.00001439339,0.00005706308,0.00009037154,0.0000394512],"domain_scores_gemma":[0.9982178,0.001358918,0.0000718201,0.0001350199,0.0001476756,0.00006871211],"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":[0.00005032053,0.00005939411,0.0007141524,0.00007994722,0.00006411663,0.00009778473,0.00006798505,0.872145,0.001575287,0.05447944,0.002000223,0.0686664],"study_design_scores_gemma":[0.00000654743,0.000007311155,0.00002528214,0.000005388492,0.000005706273,0.000009201693,0.000002954007,0.9826895,0.0002541305,0.01650374,0.0004878079,0.000002474064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02091067,0.0007650278,0.9718089,0.0008227894,0.00008830118,0.00004873381,0.0001776857,0.0006713683,0.00470642],"genre_scores_gemma":[0.5989944,0.0007993464,0.3906265,0.0007175395,0.0001566701,0.0002046345,0.0005034395,0.0002375795,0.007759962],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005028459,"threshold_uncertainty_score":0.012676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03029241096829063,"score_gpt":0.2923361754246098,"score_spread":0.2620437644563192,"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."}}