{"id":"W4294031186","doi":"10.26434/chemrxiv-2022-m3900","title":"Latent Spaces for Antimicrobial Peptide Design","year":2022,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Canada Research Chairs; Compute Canada","keywords":"Interpretability; Computer science; Artificial intelligence; Machine learning; Generative grammar; Generative model; Construct (python library); Sampling (signal processing); Chemical space; Computational biology; Biology; Bioinformatics; Drug discovery","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":[0.001395198,0.0007599171,0.0007529079,0.0007627782,0.0002934668,0.001132392,0.0007961395,0.001208987,0.003184938],"category_scores_gemma":[0.004310781,0.0005030128,0.001113685,0.0005615124,0.001074789,0.001437451,0.001237006,0.001865876,0.0007657887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009951475,"about_ca_system_score_gemma":0.0007329492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001611557,"about_ca_topic_score_gemma":0.001823405,"domain_scores_codex":[0.9994573,0.0002773353,0.00002636655,0.0000909544,0.0001010567,0.00004705876],"domain_scores_gemma":[0.9982882,0.001255616,0.0001556379,0.0001284978,0.0001144165,0.00005757868],"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":[0.00007623863,0.00003834363,0.0007847966,0.0001497149,0.00006329743,0.00003841717,0.00005038878,0.8626292,0.002906665,0.0854504,0.001267266,0.04654533],"study_design_scores_gemma":[0.000007531828,0.00002572165,0.00007717204,0.00001754188,0.000006847181,0.00001147683,0.000006053621,0.9510457,0.0007138088,0.04689297,0.001187816,0.000007413403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01075415,0.001494691,0.9848326,0.0005125918,0.0000522019,0.000028463,0.0001772607,0.0003702507,0.001777808],"genre_scores_gemma":[0.6101066,0.003203011,0.3768143,0.0006017539,0.0002369234,0.0005012479,0.001066325,0.0003419378,0.007127846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003184938,"threshold_uncertainty_score":0.01065469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03674293776938187,"score_gpt":0.2543758531816145,"score_spread":0.2176329154122326,"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."}}