{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002978184,0.001272145,0.001086344,0.001687547,0.000418864,0.0006338034,0.0008120562,0.0007657763,0.0009285095],"category_scores_gemma":[0.004673142,0.0002002316,0.0009035182,0.001082403,0.0003578634,0.0008061404,0.0006701295,0.0006998882,0.0002653257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006747816,"about_ca_system_score_gemma":0.0007056352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002102982,"about_ca_topic_score_gemma":0.001530466,"domain_scores_codex":[0.9990739,0.0003622042,0.00007218065,0.0001075755,0.0003049923,0.0000791358],"domain_scores_gemma":[0.9983727,0.0009912925,0.0001469542,0.0001275258,0.0002887787,0.00007276605],"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":[0.0005842519,0.0003278024,0.0266683,0.0005493236,0.0005146234,0.0002276745,0.0002279654,0.807762,0.1045216,0.007338893,0.002343734,0.04893375],"study_design_scores_gemma":[0.00001472452,0.0001410234,0.004419039,0.000009446808,0.00002406357,0.00004779688,0.00003006596,0.9695459,0.02419144,0.001015561,0.0005248525,0.0000362563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8307992,0.0008833219,0.1633338,0.0001728416,0.00002514222,0.00008797795,0.001304981,0.001566485,0.001826266],"genre_scores_gemma":[0.937905,0.0002718598,0.05860866,0.00004708864,0.000006106669,0.0001909377,0.002343787,0.0001900528,0.000436522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002978184,"threshold_uncertainty_score":0.01575035,"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."}}