{"id":"W3001449808","doi":"10.1002/cpt.1795","title":"Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?","year":2020,"lang":"en","type":"review","venue":"Clinical Pharmacology & Therapeutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; National Institutes of Health","keywords":"Context (archaeology); Drug discovery; Clinical pharmacology; Identification (biology); Clinical trial; Clinical Practice; Generative grammar; Drug; Pharmacology; Medicine; Data science; Artificial intelligence; Computer science; Bioinformatics; Internal medicine; Biology","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":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.006156322,0.001342121,0.004995372,0.0003164634,0.0003285934,0.0003925655,0.005443766,0.001192576,0.0001286882],"category_scores_gemma":[0.0003277218,0.001109915,0.005100097,0.001235964,0.00126911,0.00120238,0.001829718,0.003675819,0.0003647393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003697308,"about_ca_system_score_gemma":0.004072073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002087391,"about_ca_topic_score_gemma":0.000001063981,"domain_scores_codex":[0.9828554,0.00722319,0.005385237,0.002707833,0.00056301,0.001265279],"domain_scores_gemma":[0.9613384,0.03441991,0.00212777,0.0009740379,0.000382748,0.0007571086],"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":[0.0002320091,0.001171799,0.000007862667,0.0008576261,0.003793251,0.00008888463,0.00006572121,0.0004204863,0.000002440911,0.001450968,0.02800625,0.9639027],"study_design_scores_gemma":[0.0005639051,0.0008491087,0.000008678711,0.0003025689,0.005103443,0.00004410698,0.000006179264,0.05133284,0.00001489175,0.01121913,0.9295155,0.001039633],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00002117016,0.5643973,0.4060276,0.005364448,0.02134675,0.002328393,0.0002207407,0.0002678059,0.00002577152],"genre_scores_gemma":[0.00003375351,0.9316645,0.02532444,0.03173237,0.01045389,0.0003969095,0.0001202561,0.0001615891,0.000112249],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9628631,"threshold_uncertainty_score":0.9999372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3585694479664162,"score_gpt":0.5906098123856323,"score_spread":0.2320403644192161,"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."}}