{"id":"W1966882047","doi":"10.1021/ci900427b","title":"Pharmacophore Screening of the Protein Data Bank for Specific Binding Site Chemistry","year":2010,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"Canadian Institutes of Health Research; Wellcome Trust","keywords":"Pharmacophore; Protein Data Bank; Protein Data Bank (RCSB PDB); Virtual screening; Chemistry; Computational biology; Binding site; Allosteric regulation; Protein structure; Biochemistry; Biology","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":[0.001090525,0.0009918724,0.001958119,0.001149414,0.0006594924,0.00100212,0.001341534,0.0007374461,0.00739982],"category_scores_gemma":[0.003002398,0.0005208272,0.001140913,0.00157697,0.000350905,0.0006999706,0.0007315177,0.0007698818,0.001587271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008677408,"about_ca_system_score_gemma":0.003402528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003340534,"about_ca_topic_score_gemma":0.004804024,"domain_scores_codex":[0.9995664,0.0001518316,0.00002849959,0.0000713606,0.0001322374,0.00004969644],"domain_scores_gemma":[0.9992768,0.0004257773,0.0000450028,0.0001016354,0.0001222645,0.00002852162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002194809,0.001083755,0.01066798,0.00175376,0.0004782288,0.001069246,0.0002376013,0.5733908,0.05945715,0.05468009,0.03954096,0.2554455],"study_design_scores_gemma":[0.0003453247,0.0002277971,0.0008269243,0.00002564142,0.00006258627,0.0001517826,0.00004253357,0.9601557,0.01834744,0.01044761,0.009327102,0.00003949665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2778835,0.0009187449,0.6555173,0.001157636,0.0001128367,0.001937072,0.01984454,0.0210393,0.02158913],"genre_scores_gemma":[0.4705016,0.0006753475,0.5057064,0.0004274047,0.0000230583,0.002380897,0.01723447,0.0005994344,0.002451462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00739982,"threshold_uncertainty_score":0.02475488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06301424590899346,"score_gpt":0.3262644707679697,"score_spread":0.2632502248589763,"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."}}