{"id":"W4283835519","doi":"10.1038/s41570-022-00363-z","title":"CACHE (Critical Assessment of Computational Hit-finding Experiments): A public–private partnership benchmarking initiative to enable the development of computational methods for hit-finding","year":2022,"lang":"en","type":"review","venue":"Nature Reviews Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Structural Genomics Consortium; Ontario Institute for Cancer Research; University of Toronto","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; Karolinska Institutet; National Institute of General Medical Sciences; Bayer","keywords":"Benchmarking; Computer science; Cache; Identification (biology); Computational model; Benchmark (surveying); Drug discovery; Data science; Chemical space; Machine learning; Artificial intelligence; Bioinformatics; Parallel computing; Biology; Business","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009514749,0.0008193536,0.002696726,0.0003390985,0.0005646109,0.0002244628,0.002770019,0.0004554591,0.0001722601],"category_scores_gemma":[0.002728449,0.0006649026,0.001033636,0.001961736,0.0001207731,0.0003503486,0.001785802,0.001664561,0.000002168185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001795243,"about_ca_system_score_gemma":0.003592699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.575123e-7,"about_ca_topic_score_gemma":2.791856e-7,"domain_scores_codex":[0.9912979,0.002348677,0.002904586,0.001347487,0.001412437,0.0006889314],"domain_scores_gemma":[0.9822565,0.01414418,0.002076069,0.0007963134,0.0004744522,0.0002525162],"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":[0.000005363212,0.0002096595,0.000003402537,0.01805706,0.0003986525,0.000002587083,0.0008268962,0.004248158,0.00002888804,0.01935317,0.0004886049,0.9563776],"study_design_scores_gemma":[0.0002764135,0.00003922393,0.000007339741,0.00538093,0.0002299535,0.00004025378,0.00006182702,0.01454162,0.0003566887,0.005961139,0.9723928,0.0007118004],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00000492421,0.4678477,0.5295678,0.000195204,0.0002596656,0.001371607,0.00005153681,0.00002887948,0.0006726494],"genre_scores_gemma":[0.0000993894,0.08277877,0.9141499,0.0001998031,0.0002024582,0.001745499,0.0007335808,0.00005989638,0.00003069401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9719042,"threshold_uncertainty_score":0.9995802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2852273744619017,"score_gpt":0.5337587094550026,"score_spread":0.2485313349931009,"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."}}