{"id":"W4410051124","doi":"10.1038/s41598-025-98771-w","title":"Comprehensive computational strategies for multi-target drug discovery in inflammatory bowel disease utilizing bioactive compounds","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Manitoba Hydro; Université de Saint-Boniface","funders":"Fasa University of Medical Sciences","keywords":"Drug discovery; Inflammatory bowel disease; Drug; Disease; Computational biology; Inflammatory Bowel Diseases; Bioinformatics; Computer science; Medicine; Pharmacology; Biology; Internal 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009034214,0.001212598,0.002124985,0.001333534,0.0008405558,0.001348837,0.001874622,0.001680664,0.004474803],"category_scores_gemma":[0.002213257,0.0007204648,0.001484674,0.001416826,0.0005765933,0.0008026058,0.001126689,0.001076391,0.0005740123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040129,"about_ca_system_score_gemma":0.002756301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01031415,"about_ca_topic_score_gemma":0.01189125,"domain_scores_codex":[0.9997526,0.0001099317,0.00001313493,0.00002877173,0.00006167978,0.00003391254],"domain_scores_gemma":[0.9991664,0.0006041982,0.00004571581,0.00003817321,0.00008976032,0.00005570428],"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":[0.00007065121,0.00006043677,0.0006349222,0.0002150086,0.0001142005,0.0001111468,0.00002196656,0.9795079,0.0004252467,0.007571734,0.001071959,0.01019489],"study_design_scores_gemma":[0.00002632337,0.00001945912,0.00005617837,0.00001044836,0.000021612,0.00001109455,0.00001133002,0.9955288,0.0001304183,0.003269065,0.0009116002,0.000003619558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1609325,0.01005353,0.7747453,0.003739642,0.0004339898,0.0006834614,0.003202471,0.003102622,0.04310645],"genre_scores_gemma":[0.5988705,0.004290866,0.3849172,0.0008238874,0.0001628592,0.00198212,0.003114086,0.0003949556,0.005443523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01031415,"threshold_uncertainty_score":0.02050829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0326366761206151,"score_gpt":0.3294114607077666,"score_spread":0.2967747845871515,"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."}}