{"id":"W6903108435","doi":"10.1021/ci2004779.s001","title":"Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Virtual screening; Drug discovery; Estrogen receptor; Computational model; Drug; Selective estrogen receptor modulator; Filter (signal processing); Estrogen receptor alpha","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001265655,0.0007981433,0.0006965247,0.0006609182,0.0002401528,0.0009393441,0.000599655,0.0003970354,0.002312304],"category_scores_gemma":[0.001222112,0.0003244454,0.0006653139,0.0005717926,0.0004278879,0.0008944209,0.0006000372,0.0009880573,0.0006175142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005454076,"about_ca_system_score_gemma":0.001562593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001350673,"about_ca_topic_score_gemma":0.00174702,"domain_scores_codex":[0.9996452,0.000108148,0.00001887048,0.00005702061,0.0001440248,0.00002664217],"domain_scores_gemma":[0.9995998,0.0002393058,0.00003895822,0.00004785404,0.00004120133,0.00003293558],"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":[0.0008498165,0.0008785335,0.003255726,0.0007966145,0.0002923778,0.0002897322,0.000110807,0.5653902,0.09629064,0.07070107,0.007156415,0.2539881],"study_design_scores_gemma":[0.0001804978,0.000324643,0.0005359998,0.00002670997,0.00007277243,0.00007502206,0.0000142523,0.9386448,0.03500971,0.01125062,0.01382715,0.00003776659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1180913,0.002834455,0.844467,0.001683182,0.0001899208,0.0003250059,0.001037107,0.01406998,0.01730208],"genre_scores_gemma":[0.3373602,0.003069779,0.6544312,0.0004792173,0.0001271379,0.0003325768,0.001234807,0.0005627535,0.002402384],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002312304,"threshold_uncertainty_score":0.007735431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163160030316494,"score_gpt":0.2606201234817775,"score_spread":0.2389885231786125,"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."}}