{"id":"W4408472668","doi":"10.1186/s40364-025-00758-2","title":"Integrating artificial intelligence in drug discovery and early drug development: a transformative approach","year":2025,"lang":"en","type":"review","venue":"Biomarker Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"CRIS Cancer Foundation; Ministerio de Ciencia e Innovación","keywords":"Computer science; Drug development; Drug discovery; Data science; Identification (biology); Artificial intelligence; Transformative learning; Risk analysis (engineering); Medicine; Drug; Bioinformatics; Psychology","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":[0.009378926,0.001427108,0.00179489,0.003685916,0.0007184384,0.005547199,0.002332395,0.003242711,0.002018669],"category_scores_gemma":[0.007235496,0.0006956255,0.001424023,0.002665899,0.005028519,0.006625202,0.003365744,0.006903112,0.001063394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003172211,"about_ca_system_score_gemma":0.004301231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001533564,"about_ca_topic_score_gemma":0.001226172,"domain_scores_codex":[0.9953275,0.002275893,0.0003976649,0.0004630174,0.001330721,0.0002052087],"domain_scores_gemma":[0.9914789,0.006547058,0.0003350689,0.000468238,0.0009284313,0.0002423637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007905842,0.0001913149,0.0006147278,0.01334288,0.0003396975,0.0003019994,0.0009216876,0.007389636,0.002371719,0.3971237,0.01808281,0.5592407],"study_design_scores_gemma":[0.00003134123,0.0002331372,0.0004513594,0.006000388,0.0001901284,0.0004185364,0.0004084097,0.005344636,0.002100843,0.3586573,0.6260616,0.0001022499],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001814253,0.8150594,0.119207,0.04016227,0.002408081,0.0002242887,0.0001239939,0.0003757996,0.02062497],"genre_scores_gemma":[0.0298885,0.8421113,0.1113234,0.01020759,0.00249982,0.0003367564,0.0001936801,0.00007493206,0.003364061],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009378926,"threshold_uncertainty_score":0.04960108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.189643757778319,"score_gpt":0.4537753084490421,"score_spread":0.2641315506707231,"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."}}