{"id":"W4415400619","doi":"10.1021/acs.jcim.5c00950","title":"Combining GCN Structural Learning with LLM Chemical Knowledge for Enhanced Virtual Screening","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Virtual screening; Concatenation (mathematics); Drug discovery; Context (archaeology); Support vector machine; Deep learning; Identification (biology); Feature (linguistics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004901731,0.0001117986,0.0002134997,0.0001736682,0.00009196964,0.0002054236,0.0002529852,0.00005901257,8.980726e-7],"category_scores_gemma":[0.0002833951,0.00009150265,0.00007034991,0.0002002519,0.00002871914,0.001777345,0.0001289162,0.0003009159,3.271504e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004668032,"about_ca_system_score_gemma":0.0001548678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.443117e-7,"about_ca_topic_score_gemma":5.182623e-8,"domain_scores_codex":[0.9989663,0.00002879832,0.0005538044,0.0000929899,0.0002038876,0.0001541987],"domain_scores_gemma":[0.9988065,0.0003257489,0.0002585068,0.00006826857,0.0004611532,0.00007985204],"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":[0.0002705449,0.00002157911,0.00001838024,0.00008066316,0.00007105303,4.249629e-7,0.003017957,0.7328292,0.02551142,0.03066456,0.00004909797,0.2074652],"study_design_scores_gemma":[0.001124723,0.00007121145,0.000004305421,0.0001618193,0.00001284459,0.00002569709,0.0002216495,0.956003,0.03934098,0.002754236,0.0001768799,0.000102678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3573176,0.00004277378,0.642109,0.0001586569,0.00007734327,0.00004977608,4.304107e-7,0.0000170556,0.000227326],"genre_scores_gemma":[0.8005486,0.000003389651,0.1992425,0.0001476551,0.00004111051,0.000002476155,0.000004623874,0.000002880481,0.000006858653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4432309,"threshold_uncertainty_score":0.373137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02128940414733342,"score_gpt":0.3159006461676916,"score_spread":0.2946112420203582,"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."}}