{"id":"W4412553255","doi":"10.1080/19420862.2025.2534626","title":"AlphaBind, a domain-specific model to predict and optimize antibody–antigen binding affinity","year":2025,"lang":"en","type":"article","venue":"mAbs","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alpha Technologies (Canada)","funders":"","keywords":"Antibody; Antigen; Computational biology; Chemistry; Domain (mathematical analysis); Computer science; Biology; Immunology; Mathematics","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":[0.0003452461,0.0006728189,0.0005131258,0.0002725992,0.0001824245,0.000462975,0.001072862,0.0008503117,0.001943673],"category_scores_gemma":[0.0011449,0.0003822682,0.0005920279,0.0002120127,0.0002881264,0.0005010826,0.0004140794,0.001167116,0.0006669855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006067349,"about_ca_system_score_gemma":0.0009617987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003428902,"about_ca_topic_score_gemma":0.006255006,"domain_scores_codex":[0.9998912,0.00002186367,0.000004897686,0.00003203533,0.00003256387,0.00001750605],"domain_scores_gemma":[0.9998124,0.0001001836,0.00002043334,0.00001398545,0.00003682586,0.00001617313],"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":[0.0001026648,0.00008454797,0.001916084,0.00008836998,0.00005190546,0.00006314849,0.00002258199,0.948343,0.01192132,0.003949508,0.003741341,0.02971546],"study_design_scores_gemma":[0.000009570356,0.00002354468,0.0000737106,0.000002994986,0.000005752762,0.00001083423,0.000002085508,0.9964395,0.001514728,0.00110228,0.0008119182,0.000003081793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2384956,0.001795931,0.7367185,0.001135785,0.0002405234,0.0001438748,0.002819672,0.006509101,0.01214102],"genre_scores_gemma":[0.7505823,0.0009157847,0.2267632,0.0008303894,0.00006527182,0.0004534401,0.004634277,0.0006899132,0.01506543],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003428902,"threshold_uncertainty_score":0.006817877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03172860103541417,"score_gpt":0.3407903234205937,"score_spread":0.3090617223851796,"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."}}