{"id":"W4416185076","doi":"10.14740/aicm8","title":"The Role of Artificial Intelligence in Accelerating Drug Discovery and Development","year":2025,"lang":"en","type":"article","venue":"AI in Clinical Medicine","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pharmacovigilance; Drug development; Drug discovery; Pharmaceutical industry; Clinical trial; Key (lock)","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":[0.00418175,0.00008094704,0.0002431246,0.0001152586,0.00005321037,0.00004048207,0.000496157,0.00003661327,0.000001388872],"category_scores_gemma":[0.002589826,0.00005410822,0.00001978039,0.0005201462,0.0002300973,0.0001991891,0.0003953493,0.0002884319,0.000001109929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002915005,"about_ca_system_score_gemma":0.0002544301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007421189,"about_ca_topic_score_gemma":0.0003333589,"domain_scores_codex":[0.9979136,0.0003235338,0.001112036,0.0002852731,0.000215183,0.0001503604],"domain_scores_gemma":[0.9913669,0.008211861,0.0001127655,0.0002289945,0.00004759039,0.00003186789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001697262,0.00005321835,0.01705107,0.000009060403,0.000005333424,0.000002277189,0.0006279606,0.0007341139,0.00002498467,0.1495105,0.00002048836,0.831944],"study_design_scores_gemma":[0.0002569213,0.00006614882,0.2363436,0.0004859071,0.000003075951,0.000001038519,0.000844563,0.3246854,0.001132263,0.4346,0.001469335,0.0001117628],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4377078,0.00127779,0.5439316,0.01478727,0.0008762448,0.0002598006,2.68012e-7,0.00001699459,0.001142217],"genre_scores_gemma":[0.9708336,0.00008753763,0.02820486,0.0007754944,0.00005394902,0.00001149862,5.575297e-7,0.000002339259,0.00003014589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8318323,"threshold_uncertainty_score":0.310045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07718064514434624,"score_gpt":0.4386767436431842,"score_spread":0.361496098498838,"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."}}