{"id":"W4392467754","doi":"10.1101/2024.03.03.583197","title":"A resource to enable chemical biology and drug discovery of WDR Proteins","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Structural Genomics Consortium; York University; University of Toronto","funders":"National Institute of General Medical Sciences; Office of Science; Genentech; Ontario Genomics; National Institutes of Health; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Office of Research Infrastructure Programs, National Institutes of Health; Genome Canada; Argonne National Laboratory; U.S. Department of Energy; McGill University; Bayer; Pfizer; Bristol-Myers Squibb","keywords":"Druggability; Drug discovery; Computational biology; Resource (disambiguation); Small molecule; Chemical space; Chemical genetics; Class (philosophy); Suite; Chemical biology; Biology; Computer science; Bioinformatics; Data science; Biochemistry; Gene; Artificial intelligence","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":[0.002050883,0.0008914945,0.001229878,0.002340224,0.0005741748,0.001530479,0.002142948,0.0008478469,0.02476929],"category_scores_gemma":[0.003175296,0.0006876797,0.0007756759,0.002224518,0.000415769,0.001429652,0.002075239,0.001231532,0.01659705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007703321,"about_ca_system_score_gemma":0.002018315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001550593,"about_ca_topic_score_gemma":0.001698568,"domain_scores_codex":[0.9993775,0.0001556494,0.0000482008,0.0001009566,0.0002507114,0.00006702141],"domain_scores_gemma":[0.9980617,0.0006152698,0.0001063601,0.0006932626,0.0002948159,0.0002285828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002209762,0.0007875983,0.003724357,0.002106425,0.0002378923,0.001158176,0.0002413435,0.01468633,0.1899462,0.04655661,0.3449635,0.3933818],"study_design_scores_gemma":[0.0008600496,0.000272632,0.002618083,0.0002662794,0.0001421452,0.0006017656,0.00006095879,0.0346095,0.1790409,0.02569116,0.7556984,0.0001380545],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08554548,0.007752595,0.4309672,0.005220535,0.0006809348,0.001092496,0.2120369,0.1657212,0.09098262],"genre_scores_gemma":[0.1822364,0.005509799,0.4979228,0.001231876,0.0002944932,0.001086433,0.2798238,0.007282388,0.02461201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02476929,"threshold_uncertainty_score":0.08286148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004984422697643588,"score_gpt":0.2147488866110795,"score_spread":0.209764463913436,"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."}}