{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001258033,0.0001370884,0.00007936309,0.0000277651,0.0001659336,0.0000817415,0.0005246721,0.0001069205,0.0001419783],"category_scores_gemma":[0.00005968979,0.0001417965,0.000073278,0.0001660247,0.0001199286,0.000009175776,0.0003177742,0.0002018176,0.00004421497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005018359,"about_ca_system_score_gemma":0.00007153681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007130547,"about_ca_topic_score_gemma":6.810193e-7,"domain_scores_codex":[0.9992629,0.00003610084,0.0001810652,0.0002645757,0.0001016912,0.0001536946],"domain_scores_gemma":[0.9989857,0.00001649023,0.00004923427,0.0008296759,0.00004966588,0.0000692112],"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":[0.000004446342,0.00004739692,0.00003808403,0.00004904496,0.0000496585,5.536182e-7,0.00006209265,3.683636e-7,0.9659847,0.00002222689,0.03194189,0.001799518],"study_design_scores_gemma":[0.00007851554,0.000006769764,0.000003854557,0.00004605968,0.000004523112,0.000003653702,0.0001339758,0.0001814249,0.6631907,0.00002277058,0.336216,0.0001118274],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9687296,0.01484419,0.0005630374,0.001139357,0.00005989994,0.0003429254,0.0001299765,0.0001101318,0.01408085],"genre_scores_gemma":[0.9902416,0.0007113682,0.001324499,0.00008252236,0.0001212007,0.0002647237,0.001819475,0.00002625622,0.005408331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3042741,"threshold_uncertainty_score":0.5782294,"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."}}