{"id":"W4399917261","doi":"10.1002/pro.5076","title":"Structure‐aware deep learning model for peptide toxicity prediction","year":2024,"lang":"en","type":"article","venue":"Protein Science","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; BC Centre for Disease Control; BC Cancer Agency","funders":"Investment Agriculture Foundation; Genome British Columbia; Genome Canada","keywords":"Benchmark (surveying); Computer science; Deep learning; Toxicity; Machine learning; Artificial intelligence; Peptide; Graph; Amino acid; Computational biology; Chemistry; Biology; Biochemistry; Theoretical computer science","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":[0.0004998528,0.0009606082,0.0007238853,0.0004822695,0.0002003752,0.0004721862,0.001191484,0.001072219,0.001694096],"category_scores_gemma":[0.001004678,0.0003644842,0.0007167408,0.0004744973,0.0003034556,0.0007466293,0.0005123377,0.001507991,0.0005243179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008673725,"about_ca_system_score_gemma":0.001050923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008615417,"about_ca_topic_score_gemma":0.009994487,"domain_scores_codex":[0.9998634,0.00002554617,0.000008281931,0.00004499092,0.0000284826,0.00002937143],"domain_scores_gemma":[0.999667,0.0001529543,0.00003330995,0.00001787367,0.0001081708,0.00002050177],"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":[0.0001464872,0.000161552,0.00202235,0.00008411798,0.00007373193,0.00008741132,0.00001692432,0.9009547,0.004364371,0.001634489,0.004445368,0.08600865],"study_design_scores_gemma":[0.000002805177,0.00001154673,0.00006859039,0.000002403989,0.000003768052,0.000003809525,0.000001066345,0.9987842,0.0003600552,0.0006359371,0.000124286,0.00000148492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1968882,0.004845697,0.7848013,0.001743815,0.0002565863,0.0000959223,0.001872483,0.004293871,0.005202126],"genre_scores_gemma":[0.8983256,0.001278131,0.0870545,0.0009066546,0.0001120724,0.0001873521,0.003502354,0.0001103666,0.008522826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008615417,"threshold_uncertainty_score":0.01713049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426983484856749,"score_gpt":0.2479448806952017,"score_spread":0.2336750458466342,"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."}}