{"id":"W4405181201","doi":"10.1158/1538-8514.cancerchem24-a015","title":"Abstract A015: Design and deciphering of precision peptide inhibitors for cancer stemness using generative deep learning and molecular dynamics simulations","year":2024,"lang":"en","type":"article","venue":"Molecular Cancer Therapeutics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computational biology; Peptide; Molecular dynamics; Notch signaling pathway; Molecular mechanics; Amino acid; Biology; Deep learning; Chemistry; Artificial intelligence; Biochemistry; Computer science; Receptor","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002323978,0.0002521532,0.0002224908,0.00008420611,0.0001336119,0.00009472901,0.0001002786,0.0001861455,0.000007289771],"category_scores_gemma":[0.00003987951,0.0002492303,0.00008162054,0.0001399003,0.0001029387,0.00001623276,0.00009818631,0.0001904471,1.317092e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007482694,"about_ca_system_score_gemma":0.0001568314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004404094,"about_ca_topic_score_gemma":0.00005655966,"domain_scores_codex":[0.9987991,0.00008259292,0.0003487404,0.000343389,0.0001797138,0.0002464128],"domain_scores_gemma":[0.9993804,0.00008283096,0.0001481102,0.0001668008,0.0001555126,0.00006633213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005388627,0.00001077723,0.0008124106,0.0002254642,0.0002363789,0.000002604563,0.0004267264,0.3544144,0.6125041,0.0001121459,0.000001731988,0.03119938],"study_design_scores_gemma":[0.0003534864,0.0001366913,0.0001536112,0.000198006,0.000167276,0.000006908826,0.0001238744,0.7858411,0.2098058,0.0001849656,0.002747778,0.0002804601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5031623,0.01245374,0.4838155,0.00006165666,0.0001066395,0.0003385225,0.00002757591,0.00001565649,0.0000183984],"genre_scores_gemma":[0.9599445,0.001032105,0.03861128,0.0001218866,0.00007067768,0.00004970065,0.00004894936,0.00007761383,0.00004330598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4567822,"threshold_uncertainty_score":0.999996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02081507089818918,"score_gpt":0.3244093491747327,"score_spread":0.3035942782765436,"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."}}