{"id":"W7032975839","doi":"","title":"Optimization of binding affinities in chemical space for drug discovery","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Literature and Cultural Memory","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Chemical space; Drug discovery; Docking (animal); Leverage (statistics); Oracle; Graph; Binding affinities","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":[0.0008054886,0.001179482,0.001131975,0.0008303714,0.0003475238,0.0009587745,0.001066786,0.001724635,0.003528141],"category_scores_gemma":[0.002817657,0.0008804527,0.0009873897,0.0007292805,0.001145377,0.001673306,0.001434511,0.001826798,0.001077423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00157113,"about_ca_system_score_gemma":0.001513131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003762614,"about_ca_topic_score_gemma":0.005945509,"domain_scores_codex":[0.9995691,0.0001322899,0.00001816327,0.00009874978,0.0001265101,0.00005516512],"domain_scores_gemma":[0.999328,0.0004190737,0.0000717705,0.00006543977,0.00007557543,0.00004020305],"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":[0.00004436809,0.00004973983,0.0003108025,0.00008921326,0.00003123824,0.0000224602,0.00002434031,0.9468228,0.004754437,0.01038246,0.001001572,0.03646657],"study_design_scores_gemma":[0.000005129584,0.00001856536,0.00004162167,0.000004397305,0.000004701935,0.000007328741,0.000003429965,0.9951195,0.0008143585,0.003429369,0.0005484277,0.000003191401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03390606,0.001120318,0.9568721,0.000628343,0.00005735007,0.000086202,0.0001777659,0.001239091,0.00591278],"genre_scores_gemma":[0.6760502,0.001587185,0.3141917,0.0007697535,0.00008840215,0.0003218535,0.0006097067,0.0004009051,0.005980249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003762614,"threshold_uncertainty_score":0.01180285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02161851453937685,"score_gpt":0.2230680027824682,"score_spread":0.2014494882430913,"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."}}