{"id":"W2164260704","doi":"10.1109/bibm.2009.60","title":"Neural Grammar Networks in QSAR Chemistry","year":2009,"lang":"en","type":"article","venue":"","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantitative structure–activity relationship; Parsing; Computer science; Artificial neural network; Artificial intelligence; Grammar; String (physics); Machine learning; Representation (politics); Cheminformatics; Natural language processing; Theoretical computer science; Mathematics; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002229337,0.0006658464,0.000926994,0.001339036,0.0004826029,0.001486297,0.001098098,0.001595526,0.00207754],"category_scores_gemma":[0.007974482,0.0005053041,0.0006695754,0.001802987,0.002926554,0.002478645,0.001093681,0.002032895,0.0006281697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001645367,"about_ca_system_score_gemma":0.001131496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007158077,"about_ca_topic_score_gemma":0.004376301,"domain_scores_codex":[0.9989852,0.0004539442,0.00006239404,0.0001980346,0.0002541556,0.00004621844],"domain_scores_gemma":[0.9969056,0.002506022,0.000134618,0.0001853836,0.0002275255,0.00004073888],"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":[0.00003462798,0.00003841502,0.001032224,0.0002526581,0.00007448671,0.0001258612,0.0001553787,0.4907325,0.0009172593,0.391541,0.002671534,0.112424],"study_design_scores_gemma":[0.00001249418,0.00001515975,0.0002746402,0.00004234387,0.000009749432,0.00002501762,0.00002072203,0.4939018,0.0004066374,0.4989766,0.006295869,0.00001884536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01907356,0.02273844,0.936852,0.005418777,0.0004286282,0.0000638741,0.000377758,0.0008086877,0.01423827],"genre_scores_gemma":[0.5078415,0.02572948,0.4508771,0.001590134,0.001199912,0.0004421965,0.001028626,0.0003248383,0.01096615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007158077,"threshold_uncertainty_score":0.01423281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109542896456404,"score_gpt":0.2712026267090369,"score_spread":0.2602483370633966,"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."}}