{"id":"W3116193352","doi":"10.26434/chemrxiv.13386092.v1","title":"AI-Driven Synthetic Route Design with Retrosynthesis Knowledge","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Retrosynthetic analysis; CASP; Computer science; Intuition; Artificial intelligence; Machine learning; Protein structure prediction; Cognitive science","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":[0.001249204,0.001180476,0.0007937222,0.0009202088,0.000618788,0.001257012,0.001928252,0.001352825,0.005897867],"category_scores_gemma":[0.002949258,0.0007179378,0.001075941,0.0007788564,0.001008262,0.002012652,0.00137597,0.001857144,0.001138008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185633,"about_ca_system_score_gemma":0.001868667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001691654,"about_ca_topic_score_gemma":0.003219436,"domain_scores_codex":[0.9995308,0.0001100609,0.00003089988,0.0001439678,0.0001461591,0.00003813862],"domain_scores_gemma":[0.9986973,0.0007620981,0.0001023901,0.0002209221,0.0001677436,0.00004952526],"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":[0.0002728654,0.0001861868,0.001058227,0.0007716141,0.0000805583,0.0001886701,0.0001708017,0.7448316,0.03248338,0.02916549,0.002468185,0.1883224],"study_design_scores_gemma":[0.00003665305,0.00008413148,0.00008531402,0.00002584229,0.0000264689,0.00004085116,0.00003461281,0.9615837,0.01379729,0.0191019,0.005162461,0.00002075635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05303741,0.0009771879,0.9256852,0.0005229465,0.00008742612,0.0002617695,0.0006403346,0.003730626,0.01505706],"genre_scores_gemma":[0.3413938,0.0006357871,0.6531642,0.0002451855,0.0000269968,0.0003842583,0.001072506,0.0004283848,0.002648961],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005897867,"threshold_uncertainty_score":0.01973039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03485831601277469,"score_gpt":0.27610020920928,"score_spread":0.2412418931965053,"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."}}