{"id":"W2289613708","doi":"10.1021/acs.jcim.5b00663","title":"Exploiting Multiple Descriptor Sets in QSAR Studies","year":2016,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; The Scarborough Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantitative structure–activity relationship; Molecular descriptor; Ranking (information retrieval); Computer science; Artificial intelligence; Machine learning; Partition (number theory); Training set; Set (abstract data type); Data mining; Pattern recognition (psychology); Mathematics","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":[0.00964839,0.001643,0.003367933,0.004909947,0.0006341506,0.00193209,0.002028461,0.001230552,0.001469067],"category_scores_gemma":[0.01737855,0.001047858,0.003601683,0.004356393,0.0009032282,0.003481515,0.002286578,0.002190717,0.0003658178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118404,"about_ca_system_score_gemma":0.001191678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002523338,"about_ca_topic_score_gemma":0.002187827,"domain_scores_codex":[0.9940047,0.003142934,0.0004154334,0.0004919008,0.001693585,0.0002513806],"domain_scores_gemma":[0.988919,0.008741317,0.0006151851,0.000941881,0.0006570168,0.0001257137],"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":[0.0001651942,0.000172981,0.002952051,0.0002905617,0.0005042658,0.0001042412,0.00006252186,0.9062407,0.004012517,0.01086008,0.0003507346,0.07428414],"study_design_scores_gemma":[0.00002640066,0.0001801121,0.0005868099,0.00002535884,0.0001071862,0.00003438717,0.0000230996,0.9838134,0.00223556,0.01187769,0.001046566,0.00004330898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06419042,0.002404817,0.9293881,0.0003595198,0.00006899541,0.0003079191,0.0004127307,0.0006308582,0.002236634],"genre_scores_gemma":[0.6610292,0.002109615,0.3335718,0.000250634,0.0001042003,0.0006457215,0.001258307,0.0001533511,0.0008772647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00964839,"threshold_uncertainty_score":0.05102623,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09843100805307531,"score_gpt":0.3412166612341577,"score_spread":0.2427856531810824,"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."}}