{"id":"W4387495762","doi":"10.1186/s12896-023-00815-4","title":"Target identification of small molecules: an overview of the current applications in drug discovery","year":2023,"lang":"en","type":"review","venue":"BMC Biotechnology","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Alberta Cancer Foundation","keywords":"Drug discovery; Identification (biology); Computational biology; Biology; Small molecule; Drug development; Process (computing); Business process discovery; Drug target; Drug; Computer science; Bioinformatics; Pharmacology; Work in process; Engineering; Genetics","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":[0.001090958,0.001331342,0.00176961,0.004168104,0.0003686264,0.001446941,0.001244802,0.001581931,0.00400535],"category_scores_gemma":[0.0009855838,0.0006250255,0.0008794373,0.004313557,0.0007134043,0.002000201,0.0009912079,0.002344778,0.003968823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008374262,"about_ca_system_score_gemma":0.001050475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001003035,"about_ca_topic_score_gemma":0.001141901,"domain_scores_codex":[0.999472,0.00007102134,0.00005646127,0.00009234334,0.0002594428,0.00004868622],"domain_scores_gemma":[0.9993353,0.0004077374,0.0000649022,0.00002229168,0.0001322149,0.00003741293],"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":[0.00006877957,0.0001106083,0.0001450084,0.0230443,0.00007756657,0.0001764487,0.00006673684,0.0005533558,0.006614536,0.007752578,0.01419859,0.9471915],"study_design_scores_gemma":[0.00001170291,0.000128484,0.0004143034,0.002473974,0.00008781059,0.0007460819,0.00003932229,0.0001930917,0.002267008,0.002145003,0.991463,0.00003019376],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001552391,0.99713,0.0007061079,0.0001744272,0.0001378226,0.00001286518,0.00003386657,0.00002024243,0.001629425],"genre_scores_gemma":[0.0006337243,0.9976405,0.0006855756,0.000133702,0.00009945207,0.00001665289,0.00004232973,0.000004183747,0.0007439614],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004168104,"threshold_uncertainty_score":0.01339918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07718924983126471,"score_gpt":0.3791888009106082,"score_spread":0.3019995510793435,"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."}}