{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005588604,0.0002198193,0.0004175433,0.00008073389,0.0003580304,0.00009907641,0.0002034213,0.0001038263,0.00285001],"category_scores_gemma":[0.0001647559,0.0001792144,0.0002298602,0.0001502621,0.00004755132,0.000162947,0.0003391106,0.0001626745,0.00009567833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004092409,"about_ca_system_score_gemma":0.00005274058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007171575,"about_ca_topic_score_gemma":0.000002112618,"domain_scores_codex":[0.997569,0.0002204585,0.000713397,0.0003118876,0.0008318856,0.0003533179],"domain_scores_gemma":[0.9986618,0.00008169663,0.0005449465,0.000280285,0.0002075423,0.0002237004],"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":[0.0004610174,0.0001424632,0.001764221,0.000018507,0.00004550249,0.00005642879,0.001913668,0.0004901498,0.9927899,0.00000781673,0.001943919,0.0003664271],"study_design_scores_gemma":[0.001471598,0.000146278,0.001222212,0.00008000115,0.00003786737,0.0001513303,0.0005295326,0.00006472166,0.96987,0.00000480318,0.02624298,0.0001786812],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875702,0.002358719,0.0079643,0.0001324666,0.001260886,0.00008896684,0.000009231872,0.0000275543,0.0005876414],"genre_scores_gemma":[0.9945835,0.00002788597,0.003213714,0.0002312421,0.0002710524,0.000004620602,0.000009016536,0.00001872075,0.001640182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02429906,"threshold_uncertainty_score":0.9980615,"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."}}