{"id":"W4319348401","doi":"10.1007/978-3-031-20730-3_25","title":"Deep Learning Model for Prediction of Compound Activities Over a Panel of Major Toxicity-Related Proteins","year":2023,"lang":"en","type":"book-chapter","venue":"Computational methods in engineering & the sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Toxicity; Drug discovery; Drug development; Quantitative structure–activity relationship; Drug; Computational biology; Drug toxicity; Pharmacology; Computer science; Chemistry; Bioinformatics; Biology; Machine learning","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":[0.0002940634,0.000678302,0.0005860087,0.0003101968,0.0001567188,0.0003700862,0.0007684306,0.0006096381,0.001977896],"category_scores_gemma":[0.0004773223,0.0002616569,0.0006437341,0.0004206448,0.0001652617,0.0004201144,0.0004131426,0.001112904,0.000628837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006974181,"about_ca_system_score_gemma":0.0007925176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01570928,"about_ca_topic_score_gemma":0.01806617,"domain_scores_codex":[0.9999281,0.00001064054,0.000004296272,0.00002215636,0.00001955042,0.00001527324],"domain_scores_gemma":[0.9998424,0.00007244328,0.00001459287,0.00001287638,0.00004646976,0.00001123072],"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":[0.0001353281,0.0001205948,0.001644918,0.00006608788,0.0001038084,0.00005753589,0.00001401574,0.8115139,0.003946741,0.003153843,0.008929981,0.1703132],"study_design_scores_gemma":[0.000003047474,0.00001185127,0.0001746567,0.000003480504,0.000007427957,0.000004518972,0.000001211032,0.9976369,0.0004720299,0.001382588,0.0003003697,0.000001907912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2226928,0.005979745,0.7471505,0.001771414,0.0003066284,0.00008791702,0.005971733,0.004562737,0.01147658],"genre_scores_gemma":[0.8649802,0.002059237,0.09858586,0.0005978764,0.0001319731,0.0001608069,0.007890325,0.0001517737,0.02544195],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01570928,"threshold_uncertainty_score":0.03123569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0795325124539135,"score_gpt":0.3388594126431883,"score_spread":0.2593269001892747,"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."}}