{"id":"W4308179880","doi":"10.1007/s11030-022-10555-w","title":"Can machine learning ‘transform’ peptides/peptidomimetics into small molecules? A case study with ghrelin receptor ligands","year":2022,"lang":"en","type":"article","venue":"Molecular Diversity","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University; Lawson Health Research Institute; Lakehead University; Western University; University of British Columbia, Okanagan Campus; University of British Columbia; Thunder Bay Regional Research Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Peptidomimetic; Small molecule; Peptide; Chemistry; Computational biology; Drug discovery; Molecule; Combinatorial chemistry; Artificial intelligence; Computer science; Biology; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0009495104,0.0002842183,0.0002864915,0.0002550645,0.001623502,0.0001444265,0.00111784,0.00003654051,0.00003317322],"category_scores_gemma":[0.00005290885,0.0003020078,0.0001399403,0.0009523687,0.00006432406,0.0001697167,0.003449156,0.0006268346,0.000009405233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003154197,"about_ca_system_score_gemma":0.0001883408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003635741,"about_ca_topic_score_gemma":0.0005789384,"domain_scores_codex":[0.997153,0.0006658401,0.0002385482,0.0007397206,0.000835065,0.0003678324],"domain_scores_gemma":[0.9988474,0.0001437881,0.000135843,0.0005328885,0.0001408104,0.0001993075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003567183,0.001499293,0.02453176,0.00005514776,0.0005139471,0.03180428,0.0391023,0.8790185,0.001904682,0.003981145,0.0001162859,0.01711597],"study_design_scores_gemma":[0.01021248,0.008646262,0.002321473,0.00004353315,0.0004950381,0.004501229,0.02450947,0.9294909,0.008983452,0.005634963,0.002402448,0.002758786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6388526,0.00006063575,0.3597175,0.0006181693,0.00008497421,0.0003959733,0.00001304567,0.0001262454,0.0001307967],"genre_scores_gemma":[0.9297838,0.000002644975,0.06965838,0.000367283,0.00001350704,0.00002757765,0.00002375414,0.00002320346,0.0000998981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2909311,"threshold_uncertainty_score":0.9999432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01482646834594805,"score_gpt":0.2377426842926379,"score_spread":0.2229162159466898,"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."}}