{"id":"W3027975282","doi":"10.1021/acscentsci.0c00229","title":"Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery","year":2020,"lang":"en","type":"article","venue":"ACS Central Science","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":425,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Cancer Society Research Institute","keywords":"Docking (animal); Drug discovery; Virtual screening; Computer science; Chemical database; Deep learning; Artificial intelligence; Training set; Computational biology; Machine learning; Bioinformatics; Biology; Medicine","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":[0.001556488,0.00160173,0.001234263,0.0009939082,0.0009209649,0.001508777,0.004377968,0.001637971,0.0323059],"category_scores_gemma":[0.003916163,0.00108467,0.001734456,0.001226448,0.00061213,0.002005666,0.003255631,0.005194927,0.009032927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001177696,"about_ca_system_score_gemma":0.003425039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006908355,"about_ca_topic_score_gemma":0.01595036,"domain_scores_codex":[0.999415,0.0001246083,0.00003603408,0.0001147006,0.0002289783,0.00008073934],"domain_scores_gemma":[0.9992536,0.0002701465,0.0000452203,0.0002126179,0.0001363849,0.00008196604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001409396,0.001003923,0.002612833,0.002373745,0.0008995084,0.0004949363,0.0002063615,0.2387337,0.04235063,0.05158496,0.3216806,0.3366495],"study_design_scores_gemma":[0.0002757414,0.0002420706,0.0004872551,0.00008980409,0.00008179228,0.0001413752,0.00002667969,0.8958052,0.02416085,0.02816637,0.05042299,0.00009988701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01789391,0.001002975,0.8326538,0.001090211,0.0007142724,0.0006886154,0.02263214,0.1082512,0.01507284],"genre_scores_gemma":[0.1452183,0.001721213,0.7645129,0.001312856,0.0001302805,0.002813454,0.04974215,0.01195061,0.02259824],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0323059,"threshold_uncertainty_score":0.108074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139345603797667,"score_gpt":0.2861229903752512,"score_spread":0.2647295343372746,"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."}}