{"id":"W4206693359","doi":"10.1021/acs.jcim.1c00765","title":"Reliable <i>In Silico</i> Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA","year":2022,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity","funders":"Biotechnology and Biological Sciences Research Council; Engineering and Physical Sciences Research Council","keywords":"In silico; Binding affinities; Affinities; Computational biology; T-cell receptor; Pharmacophore; Molecular dynamics; Ranking (information retrieval); Chemistry; Biology; Computer science; Bioinformatics; T cell; Genetics; Gene; Receptor; Machine learning; Biochemistry; Computational chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003136672,0.0000770531,0.0001518636,0.0001057093,0.00003946945,0.00002278377,0.00009719744,0.00003835218,0.000005927014],"category_scores_gemma":[0.0000182482,0.00006246064,0.0000458623,0.00008416172,0.00001198112,0.00005503891,0.00005868366,0.0001549037,1.137949e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001556686,"about_ca_system_score_gemma":0.0000552655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003780704,"about_ca_topic_score_gemma":1.781767e-7,"domain_scores_codex":[0.9991957,0.000008569415,0.0005010015,0.00003536019,0.0001550232,0.000104376],"domain_scores_gemma":[0.9995552,0.000008674904,0.0002533925,0.00006839353,0.00008711452,0.00002725181],"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":[0.0009281111,0.00008083768,0.0005798963,0.0002404373,0.0001271896,9.076775e-7,0.00306854,0.3800465,0.6094728,0.000414427,0.0001354827,0.004904898],"study_design_scores_gemma":[0.004431566,0.0006875806,0.00002348135,0.0001550518,0.00005766369,0.0003370262,0.009585327,0.6502376,0.329228,0.0002078661,0.004690018,0.0003589127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923574,0.0005224467,0.006624851,0.00006485384,0.00003393253,0.00005738829,0.000003611811,0.000002129914,0.0003333908],"genre_scores_gemma":[0.9981829,0.0001775345,0.001461174,0.0001208132,0.00001836813,0.000003484294,0.00002431343,0.000005298185,0.000006149396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2802449,"threshold_uncertainty_score":0.2547071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602711388368601,"score_gpt":0.2159166076471213,"score_spread":0.1998894937634353,"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."}}