{"id":"W4410124772","doi":"10.1021/acsmedchemlett.4c00622","title":"Allosteric Covalent Inhibitors of the STAT3 Transcription Factor from Virtual Screening","year":2025,"lang":"en","type":"article","venue":"ACS Medicinal Chemistry Letters","topic":"Cytokine Signaling Pathways and Interactions","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Institute of Genetics; National Research, Development and Innovation Office; University of Toronto Mississauga; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; University of Toronto; Budapesti Műszaki és Gazdaságtudományi Egyetem; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Magyar Tudományos Akadémia","keywords":"Allosteric regulation; Virtual screening; Transcription factor; STAT3; Transcription (linguistics); Covalent bond; Chemistry; Computational biology; Computer science; Enzyme; Biochemistry; Biology; Drug discovery; Phosphorylation; Gene","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":[0.0003265562,0.0007537003,0.0004197291,0.0003448389,0.0002858846,0.0005469759,0.000538995,0.0002889046,0.004673415],"category_scores_gemma":[0.0002785637,0.0001905806,0.0003967344,0.0003888338,0.0003542166,0.0002823246,0.0007146795,0.000789463,0.0007420014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004168649,"about_ca_system_score_gemma":0.0004302881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004521822,"about_ca_topic_score_gemma":0.001555142,"domain_scores_codex":[0.9998184,0.00002816956,0.000008411404,0.00003367576,0.00005791627,0.00005346244],"domain_scores_gemma":[0.9999367,0.00001388782,0.00001243538,0.000009104763,0.00001092934,0.00001693742],"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":[0.00166997,0.001560539,0.00194616,0.001393321,0.0002469144,0.001040441,0.0001820681,0.04305283,0.8028547,0.009747007,0.008469396,0.1278367],"study_design_scores_gemma":[0.001119959,0.006941321,0.00265909,0.0001168033,0.0003443448,0.001338594,0.000108242,0.03708767,0.8984469,0.002785451,0.04893655,0.000115093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9076098,0.01044186,0.04439309,0.0009509777,0.0003585271,0.0007306479,0.002232522,0.001362415,0.03192015],"genre_scores_gemma":[0.979419,0.005014471,0.007850452,0.0003298925,0.0000383245,0.0001886886,0.001635461,0.00005282308,0.005470887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004673415,"threshold_uncertainty_score":0.01563418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01592198671397647,"score_gpt":0.2568645545986542,"score_spread":0.2409425678846777,"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."}}