{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00183465,0.001340001,0.001110609,0.0006352872,0.0004248787,0.001535878,0.0007937673,0.000786189,0.0065309],"category_scores_gemma":[0.003538503,0.0005701201,0.001046517,0.0005386776,0.0003110894,0.0006799747,0.0005771583,0.001328802,0.003847517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006324994,"about_ca_system_score_gemma":0.0009911549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001555655,"about_ca_topic_score_gemma":0.003525586,"domain_scores_codex":[0.9990523,0.0002585365,0.00006031272,0.0001540007,0.0003624252,0.0001124102],"domain_scores_gemma":[0.9991283,0.0003520622,0.000133383,0.0001495655,0.0001879754,0.00004881608],"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":[0.001348153,0.0004233797,0.008843312,0.001128301,0.0002782244,0.0002149466,0.00008892646,0.08210497,0.8196175,0.003945366,0.01260798,0.06939892],"study_design_scores_gemma":[0.0001163883,0.0005824103,0.002766678,0.00006947142,0.0001404325,0.0002161128,0.00005917352,0.2221256,0.7617977,0.002046363,0.0100071,0.0000726619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5197236,0.003355911,0.4222842,0.001206506,0.0003578559,0.0005456401,0.01641912,0.01139315,0.0247141],"genre_scores_gemma":[0.8001204,0.001685023,0.1783468,0.0004389449,0.00003723854,0.000371166,0.01348314,0.00114261,0.004374772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0065309,"threshold_uncertainty_score":0.02184808,"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."}}