{"id":"W4412163832","doi":"10.1158/1557-3265.aimachine-a001","title":"Abstract A001: Harnessing machine learning for the virtual screening of natural compounds as both EGFR and HER2 inhibitors in colorectal Cancer: A novel therapeutic approach","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Colorectal Cancer Treatments and Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Colorectal cancer; Medicine; Cancer; Virtual screening; EGFR inhibitors; Cancer research; Bioinformatics; Internal medicine; Epidermal growth factor receptor; Biology; Drug discovery","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":[0.0008574863,0.001092157,0.001092748,0.0008309301,0.0002322971,0.0008328914,0.000721612,0.0005800505,0.001257705],"category_scores_gemma":[0.001123403,0.0002260709,0.001125886,0.0006926084,0.0002251051,0.0006947327,0.0008053961,0.0009407103,0.0004445329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003275403,"about_ca_system_score_gemma":0.0005883859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001691491,"about_ca_topic_score_gemma":0.001499806,"domain_scores_codex":[0.9996008,0.0001557224,0.00001839798,0.00007538045,0.0001094615,0.00004017933],"domain_scores_gemma":[0.9996773,0.0001231325,0.00004239961,0.00004159639,0.00007913408,0.00003638986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002891307,0.0003276202,0.003128857,0.0002587503,0.0003444443,0.0001068576,0.00002858932,0.8062409,0.01648453,0.003043261,0.003857212,0.1658898],"study_design_scores_gemma":[0.000007151762,0.0001321546,0.0002091451,0.000004921995,0.00001875398,0.000015417,0.000003470412,0.9958055,0.002531475,0.0007312489,0.0005330322,0.000007617862],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.2985348,0.004356723,0.680507,0.001107081,0.000250069,0.0002884839,0.00195298,0.005869422,0.007133416],"genre_scores_gemma":[0.8746012,0.001205234,0.118247,0.0003063092,0.0001087994,0.0002264224,0.002444644,0.000114199,0.002746128],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.001691491,"threshold_uncertainty_score":0.0045349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1491537711549479,"score_gpt":0.4877908820314224,"score_spread":0.3386371108764745,"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."}}