{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007546717,0.0005427111,0.0006259131,0.003865399,0.0007373941,0.001077513,0.0005806225,0.0003474769,0.002246887],"category_scores_gemma":[0.003815117,0.0002506397,0.0005783174,0.007823491,0.0002933228,0.0006513657,0.0007430954,0.0005186625,0.001173199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006719464,"about_ca_system_score_gemma":0.00132889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004002842,"about_ca_topic_score_gemma":0.00703679,"domain_scores_codex":[0.9982127,0.0003264313,0.0002298083,0.0004538195,0.0006512696,0.0001259846],"domain_scores_gemma":[0.9984591,0.0006257743,0.0002332507,0.0003345176,0.00028655,0.00006080555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002006435,0.0005447253,0.4276164,0.004628656,0.001659213,0.002401223,0.001155898,0.02481993,0.1189858,0.01600244,0.09902397,0.3011554],"study_design_scores_gemma":[0.0002531826,0.0004969139,0.403464,0.0005521415,0.001154244,0.003878413,0.001397522,0.1579554,0.09321626,0.0124205,0.3250411,0.0001703544],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7691304,0.0114361,0.03759616,0.0004080463,0.00009710171,0.0001824418,0.1627094,0.008833314,0.009606925],"genre_scores_gemma":[0.6779573,0.002844501,0.07019541,0.0001322146,0.0000301131,0.0001988206,0.2466435,0.0006180595,0.00138006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004002842,"threshold_uncertainty_score":0.007959068,"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."}}