{"id":"W2467466454","doi":"10.1021/acs.jcim.6b00043","title":"Assessment of Solvated Interaction Energy Function for Ranking Antibody–Antigen Binding Affinities","year":2016,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Québec Consortium for Drug Discovery","keywords":"Affinities; Affinity maturation; Alanine scanning; Binding affinities; Computational biology; Virtual screening; Function (biology); Chemistry; Point mutation; Mutant; Transferability; Pharmacophore; Antibody; Biology; Genetics; Mutagenesis; Computer science; Stereochemistry; Gene; Biochemistry; Receptor; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003049197,0.00006703931,0.0002023375,0.0002069578,0.00003716991,0.00001972948,0.00003341754,0.0000553171,0.00002446149],"category_scores_gemma":[0.00008265019,0.00004051545,0.00009771206,0.00005291953,0.00002591659,0.0006427444,0.00001954259,0.00009009861,4.395161e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003919299,"about_ca_system_score_gemma":0.00007743996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000896822,"about_ca_topic_score_gemma":1.922359e-7,"domain_scores_codex":[0.999037,0.000009430254,0.0005605112,0.00003909088,0.0002471124,0.0001068192],"domain_scores_gemma":[0.9990297,0.0001018498,0.0002834165,0.0000356131,0.0004806537,0.00006875869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001203792,0.00003724497,0.001224335,0.0001994714,0.0001247863,6.652734e-7,0.0001577807,0.0002174752,0.9508432,0.002079827,0.0001079248,0.04380349],"study_design_scores_gemma":[0.006018295,0.0009487268,0.0006697535,0.002080335,0.0001583635,0.0002786754,0.0009456926,0.6152684,0.3593727,0.001418616,0.01260226,0.0002382005],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7421522,0.00005201414,0.2567811,0.0004227904,0.00009595515,0.00004630748,0.000004762554,0.000004532645,0.0004403521],"genre_scores_gemma":[0.9966816,0.0005570728,0.002469677,0.0001153502,0.0001128107,0.000001705862,0.00002504898,0.000004031727,0.00003272412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6150509,"threshold_uncertainty_score":0.1652172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05316467262200644,"score_gpt":0.3672465738000488,"score_spread":0.3140819011780424,"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."}}