{"id":"W3167237074","doi":"10.3791/62414","title":"Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source","year":2021,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Enzyme Structure and Function","field":"Materials Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"European Commission; Diamond Light Source","keywords":"Workflow; Computer science; Scope (computer science); Identification (biology); Process (computing); Data science; Drug discovery; Fragment (logic); Nanotechnology; World Wide Web; Bioinformatics; Database; Materials science; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002051731,0.0006842894,0.001142061,0.001172125,0.001337236,0.001474771,0.001791335,0.00101587,0.021613],"category_scores_gemma":[0.001747764,0.0005894978,0.0005531913,0.001236852,0.0005239729,0.001029691,0.001929362,0.001958182,0.009136842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001337088,"about_ca_system_score_gemma":0.002059213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003739943,"about_ca_topic_score_gemma":0.006053395,"domain_scores_codex":[0.9978248,0.0001655775,0.00006704005,0.0004274182,0.001292342,0.0002227086],"domain_scores_gemma":[0.9987345,0.0002786975,0.00007151579,0.0002812578,0.0004645013,0.0001695071],"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":[0.001184159,0.0004124792,0.003318005,0.0009047853,0.00008974382,0.0008654345,0.0004862302,0.003521968,0.7471254,0.0126068,0.1220648,0.1074202],"study_design_scores_gemma":[0.0005216704,0.0005126774,0.003057031,0.0001012514,0.0000369783,0.001297474,0.0001239279,0.02110968,0.7532383,0.003120844,0.2167433,0.0001368507],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.277703,0.009738925,0.4658666,0.00421213,0.0009516737,0.002619892,0.05263751,0.0594074,0.126863],"genre_scores_gemma":[0.3513063,0.003335413,0.5716575,0.001312584,0.0001150678,0.002640884,0.04178844,0.004765159,0.02307863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.021613,"threshold_uncertainty_score":0.0723027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02357676116916384,"score_gpt":0.3517467620565828,"score_spread":0.328170000887419,"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."}}