{"id":"W4308120119","doi":"10.3390/cimb44110361","title":"In Silico Identification of Promising New Pyrazole Derivative-Based Small Molecules for Modulating CRMP2, C-RAF, CYP17, VEGFR, C-KIT, and HDAC—Application towards Cancer Therapeutics","year":2022,"lang":"en","type":"review","venue":"Current Issues in Molecular Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal; Hôpital Maisonneuve-Rosemont","funders":"","keywords":"In silico; Pyrazole; Identification (biology); Chemistry; Computational biology; Cancer research; Biology; Stereochemistry; Biochemistry; Gene","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009224543,0.0003868633,0.0009849205,0.000538879,0.00006291414,0.00006260748,0.0009164263,0.0001872266,0.000003507269],"category_scores_gemma":[0.0002185956,0.0003988322,0.0001965758,0.0008713062,0.0001062266,0.0001081825,0.0004021033,0.0003428751,5.148973e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002773113,"about_ca_system_score_gemma":0.0007266059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001410419,"about_ca_topic_score_gemma":0.00001944486,"domain_scores_codex":[0.9966623,0.0007944919,0.001087709,0.0009382677,0.0001946842,0.0003225846],"domain_scores_gemma":[0.9979908,0.0004954225,0.0008120464,0.000553734,0.00009672788,0.00005131149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000732478,0.0001016194,0.00002996561,0.002309644,0.0000357235,0.000001184236,0.0003500197,0.009068766,0.0009790384,0.009555598,0.000002632234,0.9775585],"study_design_scores_gemma":[0.0009348401,0.0001415578,0.0001316522,0.002464147,0.0001628113,0.000004876257,0.0000206627,0.4776492,0.002986613,0.03726861,0.4773546,0.0008803584],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0003929198,0.4969491,0.5012673,0.0001306259,0.0002400612,0.0009733652,0.00002565676,0.00001769926,0.000003355271],"genre_scores_gemma":[0.001330262,0.8655716,0.1305815,0.0001070399,0.0001165422,0.001710171,0.0004969054,0.00007671667,0.000009324101],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9766781,"threshold_uncertainty_score":0.9998463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183435036276453,"score_gpt":0.4631923286981856,"score_spread":0.3448488250705403,"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."}}