{"id":"W4385781690","doi":"10.1101/2023.08.10.552845","title":"Pretrainable Geometric Graph Neural Network for Antibody Affinity Maturation","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":"HEC Montréal; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Graph; Computer science; Artificial neural network; In silico; Antibody; Artificial intelligence; Computational biology; Pattern recognition (psychology); Chemistry; Theoretical computer science; Biology; Biochemistry; Immunology; Gene","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":[0.0005800279,0.001158124,0.0007419551,0.0004359147,0.0001992234,0.000432893,0.001479906,0.001043088,0.002158002],"category_scores_gemma":[0.00171769,0.000436408,0.0006529466,0.000392603,0.0005752512,0.0009305988,0.0008465721,0.001760316,0.0005577251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112411,"about_ca_system_score_gemma":0.0007779684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005963581,"about_ca_topic_score_gemma":0.007597944,"domain_scores_codex":[0.9997435,0.00005984384,0.000009337914,0.00008518294,0.00005965034,0.00004242321],"domain_scores_gemma":[0.999569,0.0002054725,0.00004816366,0.00004870069,0.0001050763,0.00002355119],"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":[0.00005895591,0.00005836744,0.0004416202,0.0000382629,0.00003478166,0.00002623333,0.00001471496,0.9285486,0.00512536,0.00172643,0.001388105,0.06253853],"study_design_scores_gemma":[0.000002086624,0.00001962612,0.00004620558,0.000001164355,0.000003238281,0.000003846218,0.000001580176,0.9978287,0.001172595,0.0008068025,0.0001126251,0.000001520945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1553227,0.0009002424,0.8340136,0.0006131967,0.0001205858,0.00009114533,0.0002901461,0.004339073,0.004309324],"genre_scores_gemma":[0.8577154,0.0002893282,0.1350578,0.0004332091,0.00004920575,0.0001327475,0.0009228772,0.0002061376,0.005193386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005963581,"threshold_uncertainty_score":0.01185775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0409933653392216,"score_gpt":0.3003935421655233,"score_spread":0.2594001768263017,"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."}}