{"id":"W4306359299","doi":"10.56588/iabcd.v1i2.66","title":"IN SILICO SCREENING OF MAJOR CANCER DRUG TARGETS (GROWTH FACTOR RECEPTORS) FOR NATURE DERIVED PHYTOCHEMICALS","year":2022,"lang":"en","type":"article","venue":"International Association of Biologicals and Computational Digest","topic":"Cancer Treatment and Pharmacology","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Drug target; In silico; Receptor; Biology; Chemistry; Pharmacology; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001476241,0.00008243628,0.0002391055,0.00009141688,0.00004543727,0.000004325539,0.00007068107,0.00007203515,0.0005850848],"category_scores_gemma":[0.0001482001,0.00007231849,0.00008550457,0.00009967058,0.00002829678,0.00004141841,0.00005091935,0.0001406629,2.939793e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000201893,"about_ca_system_score_gemma":0.00005153813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007094275,"about_ca_topic_score_gemma":0.000008848761,"domain_scores_codex":[0.9991481,0.00004704441,0.0002993127,0.0001715107,0.0002365817,0.00009742196],"domain_scores_gemma":[0.9987417,0.0005872887,0.0003178681,0.00002245742,0.0003005452,0.00003008474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001040104,0.0008140674,0.9165692,0.00004617251,0.0004206665,0.000002123548,0.0003388548,0.001351595,0.06965238,0.002409711,0.006043168,0.001311946],"study_design_scores_gemma":[0.004642833,0.0004077764,0.9552259,0.00003958832,0.00005494222,0.000003497074,0.0001139758,0.001576562,0.02281793,0.005481465,0.009481139,0.0001543891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9869996,0.0005572844,0.000106068,0.01067266,0.0003054032,0.0003270063,0.0008547723,0.000009123723,0.0001680213],"genre_scores_gemma":[0.9969338,0.0001049898,0.0008885927,0.0008320329,0.0001095121,0.0001664801,0.0005243554,0.000005066518,0.0004352293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04683445,"threshold_uncertainty_score":0.6406268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01364780433584093,"score_gpt":0.3242777721132666,"score_spread":0.3106299677774257,"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."}}