{"id":"W4366083833","doi":"10.1080/17460441.2023.2199979","title":"Open resources for chemical probes and their implications for future drug discovery","year":2023,"lang":"en","type":"review","venue":"Expert Opinion on Drug Discovery","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Innovative Medicines Initiative; Kungliga Tekniska Högskolan; European Commission; European Federation of Pharmaceutical Industries and Associations; Ontario Institute for Cancer Research; Diamond Light Source; McGill University","keywords":"Drug discovery; Identification (biology); Computer science; Drug development; Data science; Computational biology; Expert opinion; Mechanism (biology); Chemical genetics; Phenotypic screening; Function (biology); Nanotechnology; Biochemical engineering; Biology; Drug; Bioinformatics; Small molecule; Medicine; Pharmacology; Phenotype; Engineering; Genetics","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.002990634,0.000974761,0.001886161,0.002410661,0.0005400231,0.002902236,0.002693459,0.004520327,0.03758379],"category_scores_gemma":[0.003401224,0.000587344,0.0008900749,0.002625881,0.001865239,0.00717716,0.00268644,0.005928382,0.01738803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00188803,"about_ca_system_score_gemma":0.00286912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009432777,"about_ca_topic_score_gemma":0.00174071,"domain_scores_codex":[0.9985953,0.0003639482,0.0001127658,0.0001685758,0.0005871292,0.0001723737],"domain_scores_gemma":[0.9974903,0.001220196,0.0003406921,0.0001464884,0.0005351371,0.0002672043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001130978,0.00007418388,0.00005105775,0.01239278,0.00005810273,0.0003770299,0.0000765856,0.0006459098,0.002735785,0.06777187,0.1616827,0.7540208],"study_design_scores_gemma":[0.00001837969,0.00003081662,0.00004456572,0.001417653,0.00001257742,0.0002093634,0.00002073532,0.00005323618,0.000199271,0.006897634,0.9910874,0.00000830855],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002734333,0.9570135,0.003221761,0.01345291,0.00302958,0.00008107494,0.0005155823,0.0001796119,0.0222325],"genre_scores_gemma":[0.002695864,0.9765252,0.003228677,0.007071459,0.00162364,0.0001421866,0.0006387879,0.0000394472,0.008034776],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9973065,"threshold_uncertainty_score":0.1257303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04639980926559484,"score_gpt":0.3615905360618605,"score_spread":0.3151907267962657,"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."}}