{"id":"W4387699923","doi":"10.21203/rs.3.rs-3379575/v1","title":"Mapping protein binding sites by photoreactive fragment pharmacophores","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"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; European Commission; University of Oxford","keywords":"Pharmacophore; Computational biology; Fragment (logic); Diazirine; Bromodomain; Chemistry; Photoaffinity labeling; Target protein; Binding site; Combinatorial chemistry; Biochemistry; Computer science; Biology; DNA; Histone; Gene","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":[0.0003625895,0.0004133075,0.000444798,0.0003389024,0.0003345113,0.0004831055,0.0009430934,0.0006889307,0.004234325],"category_scores_gemma":[0.0005285081,0.0004965627,0.0004524894,0.0003107461,0.0004382872,0.0004018475,0.0003184112,0.001328552,0.001502514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008295718,"about_ca_system_score_gemma":0.0003405873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001163648,"about_ca_topic_score_gemma":0.001269964,"domain_scores_codex":[0.9997504,0.00003970108,0.00001004205,0.00007445629,0.00007275808,0.00005263653],"domain_scores_gemma":[0.9996834,0.0001459794,0.00006138282,0.0000514823,0.00002186133,0.00003603348],"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":[0.0003346523,0.00003598073,0.0001074316,0.00005345016,0.000007813307,0.00006293033,0.00001937881,0.0005363099,0.9943774,0.000726097,0.0001838851,0.003554642],"study_design_scores_gemma":[0.00003246681,0.00005480104,0.0004285721,0.000004585364,0.000008315614,0.0001034071,0.000009667775,0.001351242,0.9963499,0.0001807251,0.001468774,0.000007555662],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.925751,0.00202167,0.06219165,0.0004843379,0.00005020209,0.00009842656,0.0006320886,0.0006750066,0.008095654],"genre_scores_gemma":[0.9605885,0.001174547,0.02864961,0.0002091019,0.00001487075,0.0001074169,0.0009060986,0.0002062928,0.008143445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004234325,"threshold_uncertainty_score":0.01416516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07113698758305294,"score_gpt":0.3887184148305832,"score_spread":0.3175814272475303,"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."}}