{"id":"W2771893430","doi":"10.1039/c7sc03628k","title":"Efficient prediction of reaction paths through molecular graph and reaction network analysis","year":2017,"lang":"en","type":"article","venue":"Chemical Science","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Supercomputing Center, Korea Institute of Science and Technology Information; National Research Foundation of Korea; Korea Institute of Science and Technology Information; Ministry of Science, ICT and Future Planning","keywords":"Power graph analysis; Computer science; Graph; Network analysis; Chemistry; Computational chemistry; Combinatorial chemistry; Theoretical computer science; 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":[0.0005546031,0.001198289,0.0007566992,0.002880541,0.0006151018,0.0007113114,0.0007674218,0.0007613363,0.002744288],"category_scores_gemma":[0.002361293,0.0005934137,0.001071341,0.001075293,0.0006483396,0.001142442,0.000512928,0.000803658,0.000414295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106054,"about_ca_system_score_gemma":0.001870413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004993672,"about_ca_topic_score_gemma":0.007722037,"domain_scores_codex":[0.9997494,0.00007569142,0.0000132853,0.00007143986,0.00006298758,0.00002727314],"domain_scores_gemma":[0.9988787,0.000747835,0.0001531715,0.00008530077,0.00009994864,0.00003518257],"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":[0.0000565006,0.00004501412,0.00189513,0.0001756843,0.00004035244,0.00009988363,0.0000258396,0.9593184,0.004020883,0.01225192,0.0005455878,0.02152489],"study_design_scores_gemma":[0.00000546115,0.00001181605,0.0001583548,0.000004866409,0.000009736944,0.00001235037,0.000006493284,0.9902839,0.001128184,0.007894004,0.0004803425,0.000004472602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1160718,0.0006033671,0.8754482,0.0002454407,0.0000275524,0.0001926653,0.001670967,0.002258861,0.003481282],"genre_scores_gemma":[0.5892664,0.0008060904,0.4051325,0.00004832301,0.0000185593,0.0003385164,0.002641456,0.0002683909,0.001479745],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004993672,"threshold_uncertainty_score":0.00992924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880021375645944,"score_gpt":0.2991979430300002,"score_spread":0.2803977292735407,"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."}}