{"id":"W3022153309","doi":"10.1021/acsomega.0c04153","title":"Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning","year":2020,"lang":"en","type":"article","venue":"ACS Omega","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Reinforcement learning; Leverage (statistics); Chemical space; Generative grammar; Scalability; Markov decision process; Process (computing); Space (punctuation)","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":[0.001011629,0.0007151439,0.0007577928,0.0003500936,0.0002399683,0.0006187983,0.0009884706,0.0008896251,0.001644504],"category_scores_gemma":[0.002439303,0.0004453426,0.0004865341,0.0002708066,0.001225856,0.0008175549,0.0009581219,0.001374493,0.0002234449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001100853,"about_ca_system_score_gemma":0.000999775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00228614,"about_ca_topic_score_gemma":0.003740929,"domain_scores_codex":[0.9997188,0.0001077599,0.0000112927,0.00005265239,0.00007241603,0.00003692956],"domain_scores_gemma":[0.9989325,0.0007450063,0.0001156559,0.00007320747,0.00007839992,0.00005526117],"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":[0.00002576472,0.00003078402,0.0002567611,0.00003206877,0.00001385289,0.00002531737,0.00001645707,0.9795244,0.001133426,0.007170301,0.0002809572,0.01148984],"study_design_scores_gemma":[0.000006295508,0.00001080329,0.00001466551,0.000002200119,0.000001692097,0.000002578037,0.000001231595,0.9965408,0.0002275334,0.003087709,0.0001033365,0.000001124514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05763953,0.0003498223,0.9366417,0.0004201639,0.00003635134,0.00007446393,0.00005344208,0.0006382831,0.004146308],"genre_scores_gemma":[0.8438353,0.0002301488,0.1530065,0.0002837271,0.00003066854,0.0002150348,0.0001193811,0.00009876616,0.002180471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00228614,"threshold_uncertainty_score":0.007987261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02633389138114879,"score_gpt":0.2852303918269042,"score_spread":0.2588965004457555,"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."}}