{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004095581,0.0008067372,0.0009963921,0.0007509069,0.0004415064,0.0009790275,0.0009486728,0.001076757,0.004867413],"category_scores_gemma":[0.001819735,0.0004625445,0.00136215,0.0006423879,0.000448027,0.0005561133,0.0006335002,0.0009311031,0.0003831376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000830973,"about_ca_system_score_gemma":0.001903048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008593244,"about_ca_topic_score_gemma":0.01129323,"domain_scores_codex":[0.999856,0.00005182693,0.000007031836,0.00003472064,0.00002514065,0.00002523308],"domain_scores_gemma":[0.999384,0.0004715529,0.00003461596,0.00002833756,0.00004227938,0.00003904097],"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":[0.0001531243,0.00007608175,0.002240546,0.0001732444,0.0001052485,0.0001240892,0.00001913668,0.9784402,0.001015886,0.004122701,0.001712576,0.01181709],"study_design_scores_gemma":[0.0000159595,0.00002854021,0.0001363308,0.000003662649,0.00002111381,0.00001134157,0.000008292734,0.9960451,0.0002453603,0.002946328,0.0005354016,0.000002522567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7204952,0.003683661,0.2343652,0.004827226,0.0003585386,0.0003365061,0.007062604,0.003223262,0.02564781],"genre_scores_gemma":[0.9315273,0.0009598223,0.05730924,0.0006578832,0.00009257635,0.00045375,0.004456464,0.000174393,0.004368564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008593244,"threshold_uncertainty_score":0.01708645,"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."}}