{"id":"W4413944850","doi":"10.1016/j.ijpharm.2025.126117","title":"Machine learning-driven discovery of multicomponent pharmaceutical solid forms via DualNet: confidence-aware prediction and ranking of salts and cocrystals","year":2025,"lang":"en","type":"article","venue":"International Journal of Pharmaceutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alphora Research (Canada); Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Computer science; Chemistry; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002059336,0.001602642,0.002397664,0.003141,0.0006407636,0.002068539,0.002087956,0.002030581,0.002221769],"category_scores_gemma":[0.004708013,0.000593758,0.001290459,0.001761614,0.0007148652,0.001944517,0.001377514,0.001229523,0.0006410119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001310805,"about_ca_system_score_gemma":0.001608323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007104268,"about_ca_topic_score_gemma":0.01075347,"domain_scores_codex":[0.9993123,0.0001486994,0.00004107361,0.0001722349,0.0002314845,0.00009418544],"domain_scores_gemma":[0.996893,0.001911957,0.0003507313,0.0001625808,0.0004950078,0.0001867102],"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":[0.002926167,0.001545086,0.02519345,0.0009943874,0.0005070742,0.0006731796,0.00008229821,0.733164,0.01635644,0.008409751,0.0104035,0.1997447],"study_design_scores_gemma":[0.00003108608,0.00008199796,0.0002738902,0.000006523382,0.00002894403,0.00002713458,0.000008045904,0.9957208,0.001987527,0.001484398,0.0003431057,0.000006499096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8194367,0.004685151,0.1584583,0.001815274,0.0002245786,0.0002162724,0.003953654,0.00458242,0.006627603],"genre_scores_gemma":[0.9310495,0.0005892491,0.06074923,0.0002708833,0.0001066122,0.00007386984,0.005091479,0.0001409432,0.001928251],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007104268,"threshold_uncertainty_score":0.01412582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02726698877894852,"score_gpt":0.3761374705897836,"score_spread":0.3488704818108351,"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."}}