{"id":"W4401918526","doi":"10.1021/acs.jcim.4c00711","title":"Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design","year":2024,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Sequence (biology); Computational biology; Protein design; Generative model; Protein engineering; Artificial intelligence; Protein structure; Generative grammar; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001098681,0.0006755972,0.0007347468,0.000644102,0.0003687648,0.000550998,0.001593511,0.0009829033,0.002222902],"category_scores_gemma":[0.002019541,0.0005275184,0.001161271,0.0005390586,0.0007045641,0.0005504104,0.0007249052,0.00101693,0.0005002865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009275436,"about_ca_system_score_gemma":0.001102082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003597501,"about_ca_topic_score_gemma":0.005306461,"domain_scores_codex":[0.9996496,0.000122357,0.00001569812,0.00005846249,0.0001220539,0.00003177769],"domain_scores_gemma":[0.9994938,0.0003066128,0.00005431096,0.0000442979,0.00006350466,0.00003739889],"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":[0.00004785332,0.00002645177,0.0006586176,0.00004301826,0.00003328457,0.00004832335,0.00003250656,0.953145,0.002515913,0.0213857,0.001004961,0.02105842],"study_design_scores_gemma":[0.000007432053,0.00001808944,0.00003744854,0.000002791539,0.000005796745,0.00001129351,0.000001218166,0.9939096,0.0003789239,0.005094293,0.0005302327,0.000002901391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01560327,0.000264775,0.9810029,0.0001873751,0.00003343677,0.0000643169,0.0001885037,0.000623233,0.002032103],"genre_scores_gemma":[0.558057,0.0006741387,0.431326,0.0003450809,0.00007302296,0.0004824362,0.0008578578,0.0003205966,0.007863855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003597501,"threshold_uncertainty_score":0.007436335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09378072613206233,"score_gpt":0.3829621185818545,"score_spread":0.2891813924497921,"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."}}