{"id":"W4385605881","doi":"10.1016/j.biosystems.2023.104989","title":"Examining multi-objective deep reinforcement learning frameworks for molecular design","year":2023,"lang":"en","type":"article","venue":"Biosystems","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; Brock University","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Reinforcement; Engineering","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":[0.003195214,0.0008110796,0.001077196,0.0006331631,0.0003729236,0.0009176632,0.001490505,0.001651785,0.00274646],"category_scores_gemma":[0.009532105,0.0005957496,0.0004878466,0.0004127365,0.001030141,0.001367093,0.001272155,0.001673064,0.0001941756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001605027,"about_ca_system_score_gemma":0.001614762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008581996,"about_ca_topic_score_gemma":0.008344009,"domain_scores_codex":[0.9994453,0.0003305107,0.00001652089,0.00006228605,0.00008202884,0.00006337115],"domain_scores_gemma":[0.9933093,0.005751837,0.0002924893,0.0001542245,0.0003253474,0.0001668408],"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":[0.0000288924,0.00002794308,0.0002487919,0.00002254507,0.00001241171,0.000008706235,0.000009662453,0.9886939,0.00007013376,0.006369762,0.0001512135,0.00435616],"study_design_scores_gemma":[0.000003915658,0.00000891127,0.00001593624,0.000002378657,0.000001410047,7.280054e-7,0.000001522692,0.9979233,0.00001848322,0.00199267,0.00002994186,6.727096e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2602272,0.002176452,0.7226771,0.002483958,0.0001183979,0.0001180562,0.0001318322,0.0003808503,0.01168622],"genre_scores_gemma":[0.93717,0.0003910278,0.05867988,0.0002183691,0.00005434558,0.0001030437,0.0000735234,0.00005032908,0.003259406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008581996,"threshold_uncertainty_score":0.01706409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07380128584647452,"score_gpt":0.3250434305026681,"score_spread":0.2512421446561935,"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."}}