{"id":"W4412163905","doi":"10.1158/1557-3265.aimachine-a012","title":"Abstract A012: AI-assisted Design of Novel NRF2Mut Inhibitors","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Synthesis and Biological Evaluation","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Computational biology; Pharmacology; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001871355,0.0004561672,0.0003446345,0.0004638497,0.0002082305,0.0003086131,0.0004466969,0.0003504371,0.005816991],"category_scores_gemma":[0.0001547144,0.0001418016,0.0003495439,0.0004340829,0.0001296879,0.0003692024,0.0003232919,0.0005102876,0.001664999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003348099,"about_ca_system_score_gemma":0.0002766779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005446422,"about_ca_topic_score_gemma":0.00126168,"domain_scores_codex":[0.9999138,0.00001214754,0.000007869467,0.00002181692,0.00002485163,0.00001959515],"domain_scores_gemma":[0.9999586,0.000003448394,0.000009237839,0.000003682213,0.00001225249,0.00001278859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00115332,0.0009169902,0.0006488101,0.002027631,0.0001530724,0.0006827809,0.0001538891,0.007216871,0.8396128,0.009830016,0.009490686,0.1281132],"study_design_scores_gemma":[0.001011804,0.008909164,0.002090014,0.0002243922,0.0002389843,0.0009161747,0.0001102709,0.02332621,0.801563,0.002284442,0.1591945,0.0001309849],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7945099,0.03205398,0.07993072,0.002254917,0.0008536947,0.002166255,0.005974215,0.001949791,0.08030658],"genre_scores_gemma":[0.9536689,0.007965556,0.02204689,0.0005378379,0.00004812385,0.0004696627,0.001582896,0.00007024311,0.01360994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005816991,"threshold_uncertainty_score":0.01945978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5758945951923626,"score_gpt":0.5891812392391099,"score_spread":0.01328664404674729,"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."}}