{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007937711,0.001719499,0.002131318,0.001336275,0.0006441051,0.001172537,0.001039922,0.001034126,0.005942415],"category_scores_gemma":[0.001286073,0.000601706,0.003071602,0.0009950494,0.0002468146,0.0005236432,0.000563918,0.0007171294,0.001194183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000512632,"about_ca_system_score_gemma":0.001478087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003321642,"about_ca_topic_score_gemma":0.006930327,"domain_scores_codex":[0.9995091,0.0001490057,0.00003639171,0.0001089531,0.0001053266,0.00009128839],"domain_scores_gemma":[0.9994662,0.0003984876,0.00004165313,0.0000208206,0.00004101026,0.00003172269],"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":[0.003575496,0.001887316,0.06498487,0.006892215,0.002733129,0.002913009,0.0002292057,0.7244822,0.1097974,0.005899298,0.01282592,0.06377995],"study_design_scores_gemma":[0.0003503166,0.001478622,0.005985823,0.0001437029,0.001197468,0.0005122548,0.0001921745,0.9533549,0.02196651,0.002089298,0.01266255,0.00006635601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9007134,0.01118615,0.04723578,0.001007559,0.000198822,0.0005306553,0.01690033,0.005201487,0.01702578],"genre_scores_gemma":[0.9002201,0.005412623,0.06131162,0.0004726609,0.00003879426,0.0006110552,0.02656461,0.0003257544,0.005042712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005942415,"threshold_uncertainty_score":0.01987934,"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."}}