{"id":"W4414250188","doi":"10.1021/acs.jcim.5c01310","title":"DGSS: A Dynamic Interaction Graph Neural Network with Specific Substructure Awareness for Drug Synergy Prediction","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Taishan Scholar Project of Shandong Province; National Key Research and Development Program of China; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Substructure; Graph; Bridging (networking); Interaction network; Artificial neural network; Drug; Dynamic network analysis; Precision medicine","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":[0.0003177278,0.00009362939,0.0001496454,0.0001829566,0.00007809568,0.0001992003,0.0001764673,0.00004303247,6.356129e-7],"category_scores_gemma":[0.00003198838,0.00007354655,0.00006550719,0.0002582151,0.00001611045,0.002283922,0.00004539913,0.0001784985,1.006575e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000655581,"about_ca_system_score_gemma":0.00008633726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000200654,"about_ca_topic_score_gemma":7.344726e-7,"domain_scores_codex":[0.999098,0.00002595104,0.00049165,0.00008222389,0.0001887024,0.0001134983],"domain_scores_gemma":[0.9991193,0.0001325335,0.0002606606,0.00008364618,0.0003537025,0.00005016019],"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":[0.0001287105,0.000006999051,0.00002646478,0.00003436224,0.00002180205,2.63841e-7,0.0002692644,0.9702764,0.0002594519,0.003919798,0.0002155393,0.02484093],"study_design_scores_gemma":[0.0005272998,0.00002351732,0.0000564926,0.0001195225,0.00001237493,0.00006117309,0.00009110993,0.9851295,0.0005432773,0.01284662,0.0005228501,0.00006627232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3418676,0.0001030115,0.6572046,0.000341856,0.0003734462,0.00005974227,0.000001573944,0.00001513004,0.00003306284],"genre_scores_gemma":[0.9314315,0.00003959164,0.06820903,0.0002212769,0.00007555704,0.000004271386,0.0000131833,0.000002731599,0.000002846951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5895639,"threshold_uncertainty_score":0.2999141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0147468467989783,"score_gpt":0.2849495432029766,"score_spread":0.2702026964039983,"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."}}