{"id":"W2059720052","doi":"10.6026/97320630010652","title":"Structure based virtual screening of ligands to identify cysteinyl leukotriene receptor 1 antagonist","year":2014,"lang":"en","type":"article","venue":"Bioinformation","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics","keywords":"Zafirlukast; Montelukast; Leukotriene receptor; Virtual screening; Antagonist; PubChem; Pharmacology; Leukotriene; Chemistry; Leukotriene E4; Receptor; Ligand (biochemistry); Medicine; Asthma; Stereochemistry; Biochemistry; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005890423,0.001314958,0.001801199,0.001091527,0.0003947493,0.001043428,0.00102694,0.0006989504,0.003869343],"category_scores_gemma":[0.0007209699,0.0003478198,0.00113422,0.0009337736,0.0002220672,0.0003168475,0.0006860616,0.0006217521,0.0009475743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005620795,"about_ca_system_score_gemma":0.0007921926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001332766,"about_ca_topic_score_gemma":0.001832781,"domain_scores_codex":[0.9996177,0.0001377762,0.00001798395,0.00005991288,0.0001098862,0.00005668922],"domain_scores_gemma":[0.9998504,0.00007446529,0.00001960495,0.00001206311,0.00002330603,0.00002009302],"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":[0.005063765,0.002915669,0.009646317,0.002991744,0.001212755,0.001834868,0.0002245498,0.5587346,0.2452184,0.008107388,0.01824505,0.1458048],"study_design_scores_gemma":[0.001260059,0.005027019,0.005741305,0.000100509,0.0008809132,0.0008108557,0.0001977786,0.8498122,0.1056505,0.003753047,0.02664503,0.0001208249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093328,0.00888901,0.040364,0.0008958129,0.0001541281,0.001057604,0.01167771,0.005066663,0.02256222],"genre_scores_gemma":[0.9204295,0.004115361,0.05382924,0.000428644,0.00002633068,0.000805805,0.01458846,0.0001503032,0.00562633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003869343,"threshold_uncertainty_score":0.01294428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00779094139701068,"score_gpt":0.2766909135374365,"score_spread":0.2688999721404258,"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."}}