{"id":"W2950052472","doi":"10.1101/136440","title":"A Systematic Analysis of Atomic Protein-Ligand Interactions in the PDB","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"Ontario Ministry of Research, Innovation and Science; Fundação de Amparo à Pesquisa do Estado de São Paulo; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Novartis Pharma; Wellcome Trust; Genome Canada; Ontario Genomics; Pfizer","keywords":"Protein Data Bank (RCSB PDB); Ligand (biochemistry); Folding (DSP implementation); Stacking; Hydrogen bond; Protein Data Bank; Chemistry; Amide; Protein ligand; Crystallography; Small molecule; Molecule; Stereochemistry; Protein structure; Computational biology; Biochemistry; Biology; Receptor","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002819623,0.0003827194,0.001021046,0.001106946,0.0001670065,0.0006988072,0.003359328,0.0001428089,0.000003716671],"category_scores_gemma":[0.0009768225,0.0003180845,0.0004012362,0.00159023,0.0001037973,0.0004357821,0.001079919,0.000651118,0.00001564459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002433125,"about_ca_system_score_gemma":0.0006816254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001639554,"about_ca_topic_score_gemma":0.00002927909,"domain_scores_codex":[0.9963014,0.000939835,0.0009283925,0.0008466561,0.0006601043,0.0003236015],"domain_scores_gemma":[0.9941815,0.0007123273,0.001289182,0.003314827,0.0004173215,0.0000848612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001302677,0.003469711,0.02356728,0.0664332,0.02045195,0.0007066914,0.002733299,0.1554503,0.3775966,0.3490461,0.0003953919,0.00001924532],"study_design_scores_gemma":[0.0005960833,0.00004350719,0.2920578,0.01207147,0.001978571,8.844241e-8,0.00001837531,0.6543379,0.03717112,0.0003350554,0.0001077649,0.001282235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8155695,0.0004373077,0.1812445,0.0003726887,0.0006934045,0.001502225,0.00005996813,0.0001021272,0.00001825415],"genre_scores_gemma":[0.9793524,0.00001347774,0.01997814,0.00005990846,0.00006607446,0.0005014376,1.749374e-7,0.00002521248,0.000003196436],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4988877,"threshold_uncertainty_score":0.9999271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02770115983290475,"score_gpt":0.2914976140884538,"score_spread":0.263796454255549,"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."}}