{"id":"W7116926597","doi":"10.3390/ijms27010120","title":"KRASAVA—An Expert System for Virtual Screening of KRAS G12D Inhibitors","year":2025,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"Protein Kinase Regulation and GTPase Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Quantitative structure–activity relationship; Virtual screening; KRAS; Docking (animal); Test set; Applicability domain","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":[0.001331244,0.001200411,0.0009460196,0.0007870008,0.0002960962,0.0008888236,0.002211385,0.0007771179,0.01231614],"category_scores_gemma":[0.002683243,0.0005275601,0.001059229,0.0003592889,0.0003205081,0.0008513113,0.001453259,0.001097694,0.003447331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005166762,"about_ca_system_score_gemma":0.001536153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002107807,"about_ca_topic_score_gemma":0.002439006,"domain_scores_codex":[0.9995027,0.0001162917,0.00005271369,0.0001258333,0.0001456399,0.00005692311],"domain_scores_gemma":[0.9993266,0.0003480935,0.00006643059,0.00006414894,0.0001342242,0.00006045472],"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":[0.002884939,0.0008850629,0.008307487,0.002872108,0.0008367916,0.001427424,0.0004637312,0.3929997,0.06008208,0.01924806,0.1423767,0.3676159],"study_design_scores_gemma":[0.0002604377,0.0001408855,0.001241608,0.00005372555,0.00005293697,0.00018204,0.00003461578,0.9532675,0.0138613,0.006456133,0.02438199,0.00006684392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03186771,0.0004360066,0.6767557,0.0003884241,0.0001467758,0.0006129452,0.008885492,0.2742434,0.006663435],"genre_scores_gemma":[0.3642838,0.0009023021,0.5968946,0.00102925,0.00007131408,0.001668132,0.01924058,0.007889497,0.008020489],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01231614,"threshold_uncertainty_score":0.04120159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01489799097706024,"score_gpt":0.3083588786701349,"score_spread":0.2934608876930747,"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."}}