{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000951081,0.0000930495,0.000158254,0.000103909,0.0003536138,0.0001745573,0.0007647692,0.00004513319,7.735698e-7],"category_scores_gemma":[0.0003603295,0.00008593519,0.00008194199,0.001208666,0.0005551158,0.0006172479,0.000407426,0.00009972105,0.000001182341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006918836,"about_ca_system_score_gemma":0.00006820923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007085925,"about_ca_topic_score_gemma":9.129996e-7,"domain_scores_codex":[0.9984166,0.00003994106,0.0002133809,0.0005223161,0.0005845604,0.0002231897],"domain_scores_gemma":[0.9987127,0.0001020678,0.0002578767,0.0006814895,0.0001571571,0.00008866929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007782727,0.00005650129,0.001666071,0.000008024882,0.00003108174,0.00000224086,0.0001971059,0.02593617,0.9337264,0.027726,0.00001540081,0.01062726],"study_design_scores_gemma":[0.0001494691,0.00002711555,0.160312,0.00002120247,0.0000650892,0.000008907565,0.00001025577,0.6843469,0.1279412,0.02694423,0.00004809694,0.0001255876],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6300076,0.00002989789,0.3686312,0.0001457819,0.0001838456,0.00005689304,0.000001617847,0.00002864516,0.0009144961],"genre_scores_gemma":[0.9549626,0.000008190735,0.04494948,0.00002869166,0.00003843219,0.000004969781,0.000001882687,0.000002448822,0.000003371901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8057852,"threshold_uncertainty_score":0.3504336,"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."}}