{"id":"W4405181206","doi":"10.1158/1538-8514.cancerchem24-ia004","title":"Abstract IA004: Overcoming traditional design limitations with AI-based discovery","year":2024,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tolerability; Drug discovery; Cancer; Clinical trial; Medicine; Drug; Adverse effect; Cancer therapy; Computational biology; Computer science; Pharmacology; Bioinformatics; Biology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01154781,0.0008211851,0.0008930661,0.0009212457,0.0009423018,0.003610321,0.002016001,0.001304187,0.008082189],"category_scores_gemma":[0.02560834,0.0005748023,0.0008407002,0.001176142,0.002840099,0.003119414,0.002272,0.003468608,0.002527027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001346423,"about_ca_system_score_gemma":0.003880628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001257575,"about_ca_topic_score_gemma":0.001576484,"domain_scores_codex":[0.9939724,0.003524814,0.0002224088,0.0004340298,0.001717267,0.0001290746],"domain_scores_gemma":[0.9872043,0.008929761,0.0004576345,0.002036075,0.001106715,0.0002655196],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002711087,0.0001351645,0.001670809,0.001179359,0.0001961343,0.0001172226,0.0002611019,0.0877413,0.003739622,0.5458285,0.03791036,0.3209493],"study_design_scores_gemma":[0.0001440128,0.0002247439,0.0003721472,0.0003359996,0.00009434648,0.0001921611,0.00009246026,0.3186745,0.004726173,0.528032,0.1470467,0.00006474415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007833755,0.003866682,0.93516,0.01477283,0.0007786718,0.0002256392,0.0004055097,0.001874034,0.03508282],"genre_scores_gemma":[0.1639179,0.005794887,0.8108398,0.003850849,0.0003889157,0.0007984426,0.0006812341,0.0006608593,0.01306708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01154781,"threshold_uncertainty_score":0.06107134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128191650874814,"score_gpt":0.3286391155321317,"score_spread":0.2158199504446503,"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."}}