{"id":"W4411371888","doi":"10.1186/s13321-025-00958-w","title":"Crossover operators for molecular graphs with an application to virtual drug screening","year":2025,"lang":"en","type":"article","venue":"Journal of Cheminformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institutes of Health; German Network for Bioinformatics Infrastructure; Novo Nordisk Fonden; Universität Leipzig; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; Max Kade Foundation; Alexander von Humboldt-Stiftung","keywords":"Crossover; Computer science; Theoretical computer science; Context (archaeology); Algorithm; Artificial intelligence","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":[0.001353487,0.0007411654,0.0006225179,0.001512104,0.0006118241,0.001194541,0.001071545,0.0009883676,0.00239159],"category_scores_gemma":[0.003361735,0.0002423192,0.001141501,0.001544503,0.001738015,0.00103114,0.001298335,0.001856001,0.0003549886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001423549,"about_ca_system_score_gemma":0.0005987597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001074789,"about_ca_topic_score_gemma":0.0009103245,"domain_scores_codex":[0.9990332,0.0003230881,0.00005365951,0.0001394338,0.000384562,0.00006602254],"domain_scores_gemma":[0.9985363,0.0008872157,0.0001735119,0.0001950703,0.0001536389,0.00005419687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001424638,0.0001202557,0.0009200982,0.0002875804,0.00007153891,0.0003794424,0.0002506429,0.324794,0.02811134,0.4860689,0.003908447,0.1549453],"study_design_scores_gemma":[0.00007845647,0.0002058373,0.0008112388,0.00009530588,0.00006288158,0.0004980967,0.00006430344,0.649646,0.01514516,0.2996141,0.03371105,0.00006765538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01997151,0.001562885,0.971958,0.0003706219,0.0001134564,0.0001378623,0.0001683984,0.0007718625,0.004945388],"genre_scores_gemma":[0.3159996,0.002045279,0.6753637,0.0003422946,0.0001478914,0.0004008245,0.0004588987,0.0004837552,0.00475785],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00239159,"threshold_uncertainty_score":0.01032865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007400817636468401,"score_gpt":0.2996973154753661,"score_spread":0.2922964978388977,"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."}}