{"id":"W1999798000","doi":"10.1021/ci500747n","title":"Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships","year":2015,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1186,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Machine learning; Artificial neural network; Set (abstract data type); Random forest; Deep neural networks; Support vector machine; Data set; Training set; Data mining","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":[0.001608565,0.000959939,0.0006984643,0.0009855893,0.000336797,0.001137813,0.001177673,0.001111861,0.003925373],"category_scores_gemma":[0.002852386,0.0005510024,0.0007080194,0.001136501,0.0008080809,0.001307471,0.001023405,0.003054744,0.001160377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00128032,"about_ca_system_score_gemma":0.001077288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003942852,"about_ca_topic_score_gemma":0.003051549,"domain_scores_codex":[0.999222,0.0002703624,0.00004326856,0.0000939868,0.0003368613,0.00003353408],"domain_scores_gemma":[0.9993477,0.0003852554,0.00006446461,0.00007214276,0.0001064271,0.00002406182],"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":[0.0001180073,0.00009007662,0.0008329725,0.000596465,0.0001278327,0.0001297611,0.00006341545,0.489911,0.01057808,0.2495498,0.01236942,0.2356332],"study_design_scores_gemma":[0.00001080364,0.00001883502,0.0001231134,0.00003954639,0.00001038051,0.00002825741,0.000004811442,0.9425942,0.002775494,0.0448098,0.009569823,0.00001490807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002352657,0.00294878,0.9878601,0.0005544146,0.0001840598,0.00004646191,0.0003183873,0.001184472,0.004550583],"genre_scores_gemma":[0.1517286,0.00651496,0.8283086,0.0006761046,0.0002432837,0.0003750982,0.0008879315,0.0005346581,0.0107309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003942852,"threshold_uncertainty_score":0.01313174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1295232065273913,"score_gpt":0.3918566737895487,"score_spread":0.2623334672621574,"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."}}