{"id":"W2059255071","doi":"10.2174/138620706775541828","title":"Interactive Tools for Risk Reduction and Efficiency Improvements in Medicinal Chemistry","year":2006,"lang":"en","type":"article","venue":"Combinatorial Chemistry & High Throughput Screening","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Purdue Pharma (Canada)","funders":"","keywords":"Computer science; Quality (philosophy); Reduction (mathematics); Process (computing); Risk analysis (engineering); Intellectual property; Cluster analysis; Property (philosophy); Data science; Machine learning; Business","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008387685,0.0002985494,0.0003477882,0.00004484492,0.0002375137,0.0002935772,0.0005934362,0.0001563582,0.00001165002],"category_scores_gemma":[0.0005607354,0.0003287888,0.00009264913,0.0004240593,0.0001223641,0.001164808,0.0003882598,0.000396084,0.0000011797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002329128,"about_ca_system_score_gemma":0.0001293976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000364531,"about_ca_topic_score_gemma":0.000001096435,"domain_scores_codex":[0.997635,0.00008745853,0.0005379484,0.0008502189,0.0004563306,0.0004330589],"domain_scores_gemma":[0.9982423,0.0007290066,0.0003524452,0.0003991256,0.0001850249,0.00009210236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001630156,0.002235759,0.002928075,0.001169292,0.0003251113,0.0000913963,0.002293163,0.04596838,0.6295175,0.07090883,0.00239355,0.2405388],"study_design_scores_gemma":[0.009511054,0.000205035,0.005836737,0.0003394737,0.0000731149,0.00007959542,0.0003931629,0.1434141,0.6111909,0.2262563,0.001539295,0.001161237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5888234,0.0001467635,0.4084657,0.0002079394,0.001067919,0.0003163962,0.00001791039,0.0001061577,0.0008478595],"genre_scores_gemma":[0.959607,0.00000897967,0.03921784,0.00001461656,0.0009100729,0.00007758808,0.00006516295,0.00002096625,0.00007778345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3707836,"threshold_uncertainty_score":0.9999164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01328274346505063,"score_gpt":0.279148173719607,"score_spread":0.2658654302545563,"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."}}