{"id":"W4404256899","doi":"10.1093/neuonc/noae165.0500","title":"DDDR-15. UTILIZING DEEP DOCKING AND ARTIFICIAL INTELLIGENCE FOR THE DISCOVERY OF NOVEL PARP1-SELECTIVE INHIBITORS FOR USE AGAINST BRAIN TUMORS","year":2024,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Vancouver Biotech (Canada)","funders":"","keywords":"Docking (animal); Computer science; Artificial intelligence; PARP1; Drug discovery; Computational biology; Neuroscience; Bioinformatics; Biology; Medicine; Poly ADP ribose polymerase","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":[0.0005970058,0.0009500894,0.0009632247,0.000487151,0.0002641167,0.001010937,0.001037887,0.0008669168,0.004713419],"category_scores_gemma":[0.0006016805,0.0003409334,0.0008220127,0.0005441886,0.0003637827,0.000646899,0.0009313506,0.001423022,0.001818738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009819055,"about_ca_system_score_gemma":0.0009376375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002708722,"about_ca_topic_score_gemma":0.004162766,"domain_scores_codex":[0.9997405,0.00004345597,0.00001431373,0.00004929314,0.00009141224,0.00006102275],"domain_scores_gemma":[0.9998444,0.00002653361,0.00002479503,0.00001871413,0.00003953237,0.00004594792],"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":[0.001162063,0.001459654,0.004243796,0.001261324,0.0004885268,0.0007221305,0.00008986866,0.4723613,0.1506178,0.01514691,0.03360372,0.3188428],"study_design_scores_gemma":[0.0004570749,0.001711389,0.0007799011,0.00005528752,0.0001159602,0.0003849514,0.00002811986,0.8719694,0.08161309,0.004372905,0.03842871,0.00008326412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5134629,0.02244692,0.3295692,0.00614735,0.001414609,0.001256951,0.01012689,0.01988813,0.09568702],"genre_scores_gemma":[0.8038531,0.006556979,0.1525764,0.002250282,0.00006805751,0.0004505864,0.00957797,0.0005969767,0.02406967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004713419,"threshold_uncertainty_score":0.01576793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08396599421269625,"score_gpt":0.3656417017341476,"score_spread":0.2816757075214513,"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."}}