{"id":"W2028527278","doi":"10.4018/ijcmam.2014010104","title":"Rational Drug Design","year":2014,"lang":"en","type":"article","venue":"International Journal of Computational Models and Algorithms in Medicine","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Drug design; Drug; Variety (cybernetics); Class (philosophy); Rational design; Management science; Risk analysis (engineering); Data science; Artificial intelligence; Bioinformatics; Medicine; Nanotechnology; Engineering; Biology; Pharmacology","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":[],"consensus_categories":[],"category_scores_codex":[0.002699715,0.0001622661,0.0003278553,0.0006162602,0.00005079124,0.00009621968,0.0008510422,0.0000381089,0.00001504127],"category_scores_gemma":[0.0003599211,0.0001352831,0.00006321151,0.0002589006,0.0001214192,0.001089833,0.0001499995,0.0002428685,0.000002178736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008725341,"about_ca_system_score_gemma":0.0001926577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001464317,"about_ca_topic_score_gemma":0.000001185851,"domain_scores_codex":[0.9970892,0.000366686,0.0008257361,0.0002494694,0.001315941,0.0001529627],"domain_scores_gemma":[0.9963495,0.002068122,0.000408612,0.0001038995,0.0009465644,0.0001233713],"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":[0.00002696754,0.00005188218,0.00005369485,0.000003272454,0.00003434383,0.00002555728,0.0004774457,0.7481536,0.00001120246,0.1799314,0.0005016002,0.07072904],"study_design_scores_gemma":[0.0009282656,0.00006975765,0.001467137,0.00007711426,0.000003479518,0.0001994413,0.00001779907,0.5597584,0.00001350479,0.4370391,0.0003531327,0.00007282686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005338634,0.0003454557,0.9799561,0.01257228,0.001214437,0.00008043151,0.00000175394,0.00001576941,0.0004751749],"genre_scores_gemma":[0.5182989,0.00005738205,0.480183,0.0008923161,0.0005306649,0.000002832086,0.000005265378,0.00000712505,0.00002251729],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5129603,"threshold_uncertainty_score":0.5516683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04261921275402096,"score_gpt":0.340749542914402,"score_spread":0.298130330160381,"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."}}