{"id":"W4289885347","doi":"10.1039/d2sc90145e","title":"Correction: Expanding medicinal chemistry into 3D space: metallofragments as 3D scaffolds for fragment-based drug discovery","year":2022,"lang":"en","type":"erratum","venue":"Chemical Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Fragment (logic); Drug discovery; Chemical space; Chemistry; Combinatorial chemistry; Computational biology; Nanotechnology; Computer science; Materials science; Biology; Algorithm; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.004509471,0.0008699283,0.001031598,0.0002558527,0.001857909,0.001351611,0.004482435,0.0003913889,0.0120909],"category_scores_gemma":[0.003040788,0.0007880205,0.0002801217,0.001457408,0.002875698,0.001077235,0.001763761,0.000957108,0.0001575563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545727,"about_ca_system_score_gemma":0.002736894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003992363,"about_ca_topic_score_gemma":0.000006212009,"domain_scores_codex":[0.9904932,0.0001510731,0.0009595365,0.002799258,0.003927136,0.001669819],"domain_scores_gemma":[0.9962304,0.0005409333,0.0009795477,0.001326636,0.0002773089,0.0006451581],"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":[0.00007662443,0.0001129794,0.00002862868,0.0003741327,0.00000770983,0.00001637242,0.0002582993,0.0004943,0.8323203,0.00002213694,0.1660157,0.0002728823],"study_design_scores_gemma":[0.0006788067,0.0001340372,0.00001777609,0.0005436456,0.00009480565,0.00005209929,0.0002882199,0.01769918,0.8121623,0.0003067645,0.1668231,0.001199228],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.5992883,0.001438872,0.01161933,0.003250929,0.303359,0.003097808,0.0003831526,0.001423792,0.07613882],"genre_scores_gemma":[0.3203296,0.0001617702,0.06160534,0.003531308,0.01275525,0.003162181,0.001924307,0.0005421488,0.595988],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5198492,"threshold_uncertainty_score":0.9998379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007536148613244537,"score_gpt":0.2820889728229178,"score_spread":0.2745528242096733,"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."}}