{"id":"W1999552083","doi":"10.1016/j.ejmech.2010.01.002","title":"Support vector machines: Development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives","year":2010,"lang":"en","type":"article","venue":"European Journal of Medicinal Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Quantitative structure–activity relationship; Support vector machine; Molecular descriptor; Chemistry; Human immunodeficiency virus (HIV); Artificial intelligence; Artificial neural network; Biological system; Nucleoside Reverse Transcriptase Inhibitor; Nucleoside; Pattern recognition (psychology); Machine learning; Stereochemistry; Computational biology; Reverse transcriptase; Computer science; Biochemistry; Virology; Biology","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":[0.0009790853,0.0006253604,0.0009015048,0.0003859157,0.0001754802,0.000484238,0.0007466463,0.0004715752,0.00103245],"category_scores_gemma":[0.002875581,0.000289896,0.0005044285,0.0005950187,0.0001454848,0.0004130693,0.0003172827,0.001058872,0.0003689166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000294304,"about_ca_system_score_gemma":0.0006431886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002660065,"about_ca_topic_score_gemma":0.00219686,"domain_scores_codex":[0.9997097,0.0001345854,0.0000214704,0.00002766953,0.00007831072,0.00002835748],"domain_scores_gemma":[0.9991274,0.0005813821,0.00007157191,0.00002501687,0.0001680862,0.00002654032],"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":[0.0003143761,0.0001287467,0.001327429,0.0001666465,0.00008414108,0.00005263557,0.00003097625,0.8210564,0.003124259,0.001408373,0.001692348,0.1706136],"study_design_scores_gemma":[0.00001025778,0.00004379532,0.00008797377,0.000002192181,0.000006574754,0.000003987904,0.000002159904,0.9988378,0.0006484305,0.0002103088,0.0001443332,0.000002163831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.341469,0.003090822,0.647977,0.0006884899,0.0001728489,0.0002215533,0.0007711376,0.002828478,0.002780601],"genre_scores_gemma":[0.8728713,0.0009253125,0.1233076,0.00006727411,0.00003954418,0.0002150543,0.0005697847,0.00007987855,0.001924184],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002660065,"threshold_uncertainty_score":0.005289078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02780370333439076,"score_gpt":0.2932134711927009,"score_spread":0.2654097678583101,"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."}}