{"id":"W4409538420","doi":"10.1016/j.drudis.2025.104362","title":"Advances in artificial intelligence-envisioned technologies for protein and nucleic acid research","year":2025,"lang":"en","type":"review","venue":"Drug Discovery Today","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Laurentian University","funders":"","keywords":"Nucleic acid; Computational biology; Computer science; Artificial intelligence; Chemistry; Nanotechnology; Biology; Biochemistry; Materials science","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":[0.001475396,0.001234226,0.001153653,0.003240416,0.0003747633,0.001807689,0.001261631,0.002063631,0.003308346],"category_scores_gemma":[0.001486546,0.0003333798,0.0006188119,0.003345386,0.001454457,0.00344211,0.001273397,0.004097991,0.002843156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421169,"about_ca_system_score_gemma":0.001867444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001058585,"about_ca_topic_score_gemma":0.001183516,"domain_scores_codex":[0.9993351,0.0001584428,0.00005923224,0.0001034297,0.0002926104,0.00005120436],"domain_scores_gemma":[0.9988122,0.0007829245,0.00008718009,0.0000428349,0.0001962523,0.00007870914],"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":[0.00004165615,0.00009300921,0.0001435125,0.0116484,0.00008889884,0.0002224845,0.0001081196,0.0007783871,0.002048264,0.05501522,0.03026705,0.8995449],"study_design_scores_gemma":[0.000007403511,0.00005167574,0.0002323126,0.002166967,0.00003069768,0.0005596816,0.00005199974,0.0001696876,0.0004039448,0.01609194,0.9802146,0.00001908962],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00008213091,0.9948387,0.000892194,0.0008726292,0.0003441723,0.000005485441,0.00001233862,0.00001336744,0.002938996],"genre_scores_gemma":[0.0007852362,0.9965491,0.0009808111,0.0005044019,0.000374178,0.000008852165,0.00002155784,0.000003532523,0.0007721887],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003308346,"threshold_uncertainty_score":0.01106757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04574259879750615,"score_gpt":0.3873154527817341,"score_spread":0.3415728539842279,"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."}}