{"id":"W2814369124","doi":"10.1002/cmdc.201800416","title":"Cover Feature: A DNA‐Encoded Library of Chemical Compounds Based on Common Scaffolding Structures Reveals the Impact of Ligand Geometry on Protein Recognition (ChemMedChem 13/2018)","year":2018,"lang":"en","type":"article","venue":"ChemMedChem","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"","keywords":"Barcode; Cover (algebra); Ligand (biochemistry); Feature (linguistics); Scaffold; Fragment (logic); Drug discovery; DNA; Computational biology; Chemistry; Combinatorial chemistry; Computer science; Nanotechnology; Stereochemistry; Engineering; Biology; Materials science; Biochemistry; Algorithm; Database; Receptor","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":[0.0002723532,0.0004043894,0.0005868358,0.0001043152,0.00008351961,0.00004009888,0.0005200961,0.0004480077,0.0002002792],"category_scores_gemma":[0.0003344498,0.0002760128,0.0005936862,0.0004731094,0.0004696874,0.00001414342,0.0001581284,0.0002542794,0.00001161368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005006081,"about_ca_system_score_gemma":0.00009282108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002220465,"about_ca_topic_score_gemma":9.187852e-7,"domain_scores_codex":[0.9980758,0.00007789717,0.0004897508,0.0005676676,0.000395131,0.0003936933],"domain_scores_gemma":[0.9982825,0.000159514,0.0004497727,0.000818702,0.0001275747,0.0001619328],"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":[0.0007469232,0.0002835826,0.001184338,0.00008271764,0.00020139,0.00000102897,0.00002492095,0.000009695138,0.9795844,0.000007716265,0.01635149,0.001521798],"study_design_scores_gemma":[0.0008182046,0.0004246733,0.0008229798,0.0002407289,0.0000797968,0.000004294027,0.00002015033,0.0002860136,0.9962447,0.0005125706,0.000222293,0.0003235684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949636,0.0001620925,0.00001644992,0.0003552385,0.00003412738,0.0003049099,0.0001544507,0.0000234537,0.003985669],"genre_scores_gemma":[0.9979807,0.00001971426,0.0004358594,0.0003260852,0.0004824024,0.00003087919,0.0004787731,0.00005788947,0.0001876571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01666032,"threshold_uncertainty_score":0.9999692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416650541916473,"score_gpt":0.252777228703668,"score_spread":0.2386107232845033,"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."}}