{"id":"W4304128068","doi":"10.1016/j.biosystems.2022.104790","title":"Adversarial deep evolutionary learning for drug design","year":2022,"lang":"en","type":"article","venue":"Biosystems","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Brock University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Artificial intelligence; Computer science; Deep learning; Machine learning; Representation (politics); Chemical space; Space (punctuation); Process (computing); Adversarial system; Evolutionary algorithm; Set (abstract data type); Latent variable; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001400864,0.0001066679,0.0001407627,0.0001238498,0.0005876543,0.00007771672,0.0007055008,0.00002004276,0.00002000383],"category_scores_gemma":[0.0001290236,0.0001172438,0.0001025392,0.0003663871,0.0000139076,0.0002608592,0.0004213483,0.0001320575,0.0000193053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002328864,"about_ca_system_score_gemma":0.0002059426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003044065,"about_ca_topic_score_gemma":9.866496e-7,"domain_scores_codex":[0.9979428,0.0007989713,0.0002307357,0.0003724679,0.000422553,0.0002324499],"domain_scores_gemma":[0.998693,0.0008167853,0.0001233312,0.0002392659,0.00007004236,0.000057501],"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":[0.00004733949,0.0000519647,0.0001461025,0.00002499186,0.00002785389,0.000006792433,0.0009138616,0.9463426,0.0003847943,0.03880106,0.007037819,0.006214811],"study_design_scores_gemma":[0.0004071382,0.00009672686,0.0002618977,0.000005059858,0.000004585103,0.0000322363,0.0001752265,0.9273585,0.00012244,0.003747622,0.06763329,0.0001552613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003171188,0.0003596986,0.9924573,0.0004068913,0.002564943,0.0005253722,0.000006744045,0.0001944693,0.0003134296],"genre_scores_gemma":[0.8249152,8.225782e-7,0.1738227,0.00008242798,0.0003094186,0.0002620361,0.0000161239,0.00001359498,0.0005776679],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8217441,"threshold_uncertainty_score":0.4781064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02797101317999248,"score_gpt":0.2699239866194691,"score_spread":0.2419529734394767,"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."}}