{"id":"W4400838590","doi":"10.1101/2024.07.18.603797","title":"CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson’s Disease associated protein","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; Princess Margaret Cancer Centre; University of British Columbia; University of Toronto; University Health Network; Structural Genomics Consortium","funders":"Office of Science; Genentech; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; National Institutes of Health; Ontario Genomics; Office of Research Infrastructure Programs, National Institutes of Health; Genome Canada; National Institute of General Medical Sciences; Diamond Light Source; Argonne National Laboratory; European Commission; U.S. Department of Energy; McGill University; Bayer; Pfizer; Bristol-Myers Squibb","keywords":"Druggability; Docking (animal); Computer science; Cache; Computational biology; Drug discovery; Artificial intelligence; Machine learning; Bioinformatics; Chemistry; Biology; Biochemistry; Parallel computing; Medicine","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":[0.003343547,0.001285073,0.001603459,0.0005931485,0.0008698757,0.001476632,0.001821348,0.001925744,0.003275982],"category_scores_gemma":[0.004958178,0.0004169305,0.00136065,0.0008222194,0.0008660045,0.001277513,0.001640799,0.002619861,0.0008946715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093521,"about_ca_system_score_gemma":0.002535979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005166849,"about_ca_topic_score_gemma":0.009841368,"domain_scores_codex":[0.9986211,0.0004670568,0.00006178526,0.0002547897,0.000389632,0.0002056813],"domain_scores_gemma":[0.9971415,0.001370678,0.0001041347,0.0002923849,0.0007298955,0.0003613817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00397919,0.005642931,0.02344399,0.00216547,0.001020635,0.00151059,0.0005863801,0.6033992,0.05254077,0.01122574,0.1270134,0.1674716],"study_design_scores_gemma":[0.003037687,0.02081735,0.009962914,0.0002121447,0.0004754182,0.00126838,0.001087627,0.7869819,0.09667332,0.0087693,0.07037449,0.0003394879],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9555941,0.002631075,0.01845248,0.002255233,0.0004230425,0.0004532723,0.004738927,0.00235793,0.01309395],"genre_scores_gemma":[0.919275,0.001100531,0.05727785,0.00107137,0.0001202236,0.0006457315,0.01246357,0.0005500733,0.007495687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005166849,"threshold_uncertainty_score":0.01768261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01810775767393299,"score_gpt":0.2458059264474063,"score_spread":0.2276981687734733,"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."}}