{"id":"W4296301827","doi":"10.1101/2022.09.19.505646","title":"Coupling cellular drug-target engagement to downstream pharmacology with CeTEAM","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"PARP inhibition in cancer therapy","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"3v Geomatics (Canada); University of British Columbia; Université de Montréal; HEC Montréal","funders":"Canadian Institutes of Health Research; Svenska Sällskapet för Medicinsk Forskning; Cancerfonden; Science for Life Laboratory; Michael Smith Health Research BC; Pain Relief Foundation; Karolinska Institutet; Torsten Söderbergs Stiftelse","keywords":"Drug discovery; Drug; PARP1; Computational biology; Drug development; Biology; Target protein; Pharmacology; Plasma protein binding; Cell biology; Chemistry; Biochemistry; Enzyme; Poly ADP ribose polymerase; Gene","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":[0.0001859738,0.0003680308,0.0002690248,0.0001568163,0.0001331676,0.000361769,0.0003057055,0.0002948804,0.001422833],"category_scores_gemma":[0.0002200379,0.0001334703,0.0001568007,0.0001577815,0.0002784108,0.0002119698,0.0003124263,0.0007145603,0.0003822511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005153079,"about_ca_system_score_gemma":0.0002092573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004466952,"about_ca_topic_score_gemma":0.0006033412,"domain_scores_codex":[0.9998373,0.00002236114,0.00001358649,0.00005050216,0.00004943731,0.00002674987],"domain_scores_gemma":[0.9998956,0.00002484483,0.00003353065,0.00002255198,0.000009363985,0.00001407858],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007908423,0.00003487794,0.0001031247,0.00003937535,0.000005219659,0.00002947756,0.000009754322,0.0003664801,0.9961696,0.0002441021,0.00009938881,0.002819529],"study_design_scores_gemma":[0.000008132924,0.00007619805,0.0002005284,0.000001135152,0.000002043006,0.00002917029,0.000002067281,0.000906113,0.9978768,0.00002940429,0.0008666623,0.000001867918],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556047,0.001527323,0.03601488,0.0003716243,0.00007615735,0.0002256145,0.000997085,0.0006499828,0.004532544],"genre_scores_gemma":[0.9830376,0.0005854479,0.01316853,0.0001183207,0.000009873127,0.000109943,0.0003751486,0.0000447595,0.002550296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001422833,"threshold_uncertainty_score":0.004759789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186402201027958,"score_gpt":0.2671596248907752,"score_spread":0.2485194047879794,"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."}}