{"id":"W4401164887","doi":"10.1038/s42004-024-01252-w","title":"Mapping protein binding sites by photoreactive fragment pharmacophores","year":2024,"lang":"en","type":"article","venue":"Communications Chemistry","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Research, Development and Innovation Office; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Veterinärmedizinische Universität Wien; Magyar Tudományos Akadémia; Innovációs és Technológiai Minisztérium; Eötvös Loránd Tudományegyetem; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; European Commission; University of Oxford","keywords":"Pharmacophore; Computational biology; Diazirine; Fragment (logic); Chemistry; Bromodomain; Photoaffinity labeling; Target protein; Virtual screening; Binding site; Combinatorial chemistry; Biochemistry; Biology; Computer science; DNA; Gene; Histone","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":[0.0002629782,0.0003157032,0.000284864,0.0002667837,0.0001069102,0.0002060392,0.0003600545,0.0003252606,0.0009973173],"category_scores_gemma":[0.0002420818,0.0001682341,0.0002030493,0.0001992133,0.0001986506,0.0001466767,0.0002108039,0.0003088538,0.0003487926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004244141,"about_ca_system_score_gemma":0.0001537262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005441282,"about_ca_topic_score_gemma":0.000920432,"domain_scores_codex":[0.9998234,0.0000282374,0.000007428889,0.00004555844,0.00006685153,0.00002857387],"domain_scores_gemma":[0.9998885,0.00003744525,0.00003434787,0.00001275388,0.00001672094,0.00001025428],"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":[0.00004578495,0.0000156971,0.0001447173,0.00001911169,0.000003789462,0.00002214681,0.000005554022,0.0004788399,0.9968733,0.00006183053,0.00002294606,0.002306233],"study_design_scores_gemma":[0.000007457695,0.0001566244,0.000619541,0.000001656724,0.000006580979,0.0001024314,0.000005042319,0.001982203,0.9964613,0.0000278451,0.0006246754,0.000004632417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653024,0.001013899,0.03117432,0.00006921439,0.00000831812,0.00008143728,0.0002627329,0.0001888156,0.001898939],"genre_scores_gemma":[0.9734833,0.0004673895,0.0238585,0.0000461649,0.000002808254,0.00004677456,0.0002930695,0.00002338218,0.001778537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009973173,"threshold_uncertainty_score":0.00333631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818598045787878,"score_gpt":0.2871047008269281,"score_spread":0.2689187203690493,"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."}}