{"id":"W4235038979","doi":"10.1101/283069","title":"Identifying small molecule binding sites for epigenetic proteins at domain-domain interfaces","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Novartis Pharma; Ministero dello Sviluppo Economico; Fundação de Amparo à Pesquisa do Estado de São Paulo; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Ontario Ministry of Economic Development and Innovation; Genome Canada; Pfizer","keywords":"Bromodomain; Epigenetics; Computational biology; Domain (mathematical analysis); PHD finger; Histone; Small molecule; Binding site; Drug discovery; Biology; Chemistry; Genetics; Bioinformatics; Gene; Zinc finger; Transcription factor","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0004000678,0.0003477028,0.0005607445,0.000357867,0.0002456392,0.0005086472,0.0005295938,0.0004299112,0.003384921],"category_scores_gemma":[0.0005016467,0.0002264784,0.0004086551,0.0002549031,0.0002284831,0.0003240053,0.0003439554,0.0008347398,0.0008371454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005468656,"about_ca_system_score_gemma":0.0003010876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005553095,"about_ca_topic_score_gemma":0.0008624804,"domain_scores_codex":[0.999788,0.0000356805,0.0000123333,0.00004022547,0.00009107982,0.0000327752],"domain_scores_gemma":[0.9997577,0.0001220738,0.0000442517,0.00001745093,0.00002761371,0.00003094394],"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":[0.0009336908,0.000313294,0.003039776,0.0003145613,0.0000646589,0.0003119544,0.00006510266,0.01008364,0.9646855,0.001893439,0.0007630194,0.01753146],"study_design_scores_gemma":[0.0001479219,0.0003456637,0.002508408,0.00002787227,0.00005119378,0.0002672957,0.00004715508,0.03824037,0.9534543,0.0006377144,0.004252193,0.00002014098],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474192,0.002901718,0.04409817,0.0002610734,0.00003779442,0.000174897,0.0007770707,0.0005553383,0.00377466],"genre_scores_gemma":[0.9746347,0.0006335165,0.02248885,0.00009061023,0.000004198514,0.00006410404,0.0007258115,0.00002610334,0.001332061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003384921,"threshold_uncertainty_score":0.01132369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02112528886564232,"score_gpt":0.2428289451131386,"score_spread":0.2217036562474963,"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."}}