{"id":"W2165557159","doi":"10.1111/j.1747-0285.2010.01059.x","title":"Potency‐Directed Similarity Searching Using Support Vector Machines","year":2010,"lang":"en","type":"article","venue":"Chemical Biology & Drug Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Potency; Machine learning; Relevance vector machine; Similarity (geometry); Structured support vector machine; Kernel method; Kernel (algebra); Data mining; Mathematics; Chemistry","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":[0.001347252,0.0002587143,0.0003239675,0.0001316915,0.0001723514,0.0001017839,0.001254177,0.0002022525,0.00007688555],"category_scores_gemma":[0.0009030787,0.0002319379,0.0001276467,0.000458094,0.0002576813,0.0003010846,0.000549345,0.0007803271,0.0000418032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005459943,"about_ca_system_score_gemma":0.000325576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005748391,"about_ca_topic_score_gemma":0.000002948657,"domain_scores_codex":[0.9976046,0.0005644552,0.0003469012,0.0007379549,0.0002139144,0.000532154],"domain_scores_gemma":[0.9972793,0.001689995,0.0001115736,0.0005625565,0.0001461326,0.0002104222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001894995,0.00008253265,0.0006119949,0.000008652129,0.000027114,0.000016987,0.0002032699,0.00053407,0.9728343,0.006058341,0.0004121948,0.01919159],"study_design_scores_gemma":[0.0003199587,0.00002896708,0.001035488,0.000008620409,0.0000149693,0.0001006436,0.000003481195,0.5948784,0.3441251,0.05857621,0.0004974213,0.0004107637],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4101482,0.00003264244,0.5873463,0.0009116114,0.0006971358,0.0001763476,0.000009091755,0.000350952,0.0003277847],"genre_scores_gemma":[0.5438737,0.000001582046,0.455517,0.000343173,0.0001815404,0.000008747928,0.00001906566,0.00001369381,0.00004148342],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6287093,"threshold_uncertainty_score":0.9458154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03964127956195771,"score_gpt":0.3315306073263314,"score_spread":0.2918893277643737,"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."}}