{"id":"W3207372513","doi":"10.1126/sciadv.abh3794","title":"Computational repurposing of therapeutic small molecules from cancer to pulmonary hypertension","year":2021,"lang":"en","type":"article","venue":"Science Advances","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; National Institutes of Health; Gilead Sciences; National Heart, Lung, and Blood Institute; University of Pittsburgh; American Heart Association","keywords":"Drug repositioning; Repurposing; Pulmonary hypertension; Cancer; Medicine; Drug discovery; Dependency (UML); Computer science; Drug; Bioinformatics; Cardiology; Internal medicine; Pharmacology; Biology; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003777668,0.0001166745,0.0001789067,0.0001509005,0.0001966979,0.0001268134,0.0008647763,0.0000188282,0.00001169629],"category_scores_gemma":[0.0001673525,0.0001083854,0.00005233812,0.001701807,0.0002222515,0.0009730404,0.0004262559,0.00006897761,0.000007282149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006471166,"about_ca_system_score_gemma":0.0006977879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006836999,"about_ca_topic_score_gemma":0.00001807449,"domain_scores_codex":[0.998051,0.0001024387,0.0002543114,0.0006613011,0.0006834986,0.0002473736],"domain_scores_gemma":[0.9984231,0.0004565961,0.0001099868,0.0003675942,0.0005347751,0.0001079335],"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":[0.000005568196,0.00004930042,0.000485658,0.000005800261,0.000005066799,0.00002214695,0.0002542233,0.5330183,0.102635,0.007557378,0.000003523042,0.355958],"study_design_scores_gemma":[0.0001420669,0.00004233151,0.03945111,0.0001353212,0.000008621875,0.00003805045,0.000154667,0.6345893,0.1780951,0.1455887,0.001471584,0.0002831212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2643759,0.004565245,0.7285886,0.001394763,0.0005638883,0.00008870231,0.00001039057,0.00004357585,0.0003688418],"genre_scores_gemma":[0.5141578,0.0000278365,0.4848619,0.0008837983,0.00003206504,0.00000882003,0.000002986724,0.000004622402,0.00002021076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3556749,"threshold_uncertainty_score":0.4419829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810769191614291,"score_gpt":0.3290555997098777,"score_spread":0.2909479077937348,"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."}}