{"id":"W4382792660","doi":"10.1101/2023.06.29.546973","title":"Geometric Epitope and Paratope Prediction","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"European Commission","keywords":"Leverage (statistics); Paratope; Representation (politics); Epitope; Computer science; Artificial intelligence; Geometric modeling; Theoretical computer science; Mathematics; Geometry; Antibody; 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":[0.0004636973,0.0006487592,0.0006112316,0.0007436667,0.0001442905,0.0006276288,0.0006654746,0.0007453202,0.002279432],"category_scores_gemma":[0.001061265,0.0001746384,0.0004732528,0.0005358778,0.0004713318,0.0009797319,0.0006745517,0.0006654575,0.000986894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004020768,"about_ca_system_score_gemma":0.0004238312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001623486,"about_ca_topic_score_gemma":0.00129472,"domain_scores_codex":[0.9997162,0.00006685915,0.00000816028,0.00007597479,0.00008045525,0.00005235997],"domain_scores_gemma":[0.9995999,0.0001038971,0.00006544777,0.00007935307,0.0001095982,0.00004184322],"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":[0.0004570649,0.0002077866,0.008601583,0.000206824,0.00008812868,0.0001649992,0.0000298385,0.730515,0.05085135,0.01247941,0.005270244,0.1911277],"study_design_scores_gemma":[0.000007416912,0.00005414902,0.0008768549,0.000003665697,0.000004813273,0.00003864939,0.000009558572,0.9849969,0.009493096,0.003871676,0.0006373664,0.00000590174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4737017,0.001119234,0.5146332,0.0006147542,0.0001039564,0.00006767796,0.001044933,0.002370374,0.00634408],"genre_scores_gemma":[0.9260271,0.0003933511,0.07020523,0.0001056633,0.00003441327,0.00001961898,0.001476176,0.0001079804,0.001630425],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002279432,"threshold_uncertainty_score":0.007625461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03776749867560466,"score_gpt":0.2761428400163352,"score_spread":0.2383753413407305,"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."}}