{"id":"W2144342206","doi":"10.1186/s13321-015-0056-8","title":"Atom-Atom-Path similarity and Sphere Exclusion clustering: tools for prioritizing fragment hits","year":2015,"lang":"en","type":"article","venue":"Journal of Cheminformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"","keywords":"Fragment (logic); Cluster analysis; Similarity (geometry); Atom (system on chip); Path (computing); Computer science; Data mining; Information retrieval; Combinatorics; Algorithm; Artificial intelligence; Mathematics; Parallel computing","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":[0.001572759,0.0001438637,0.0002873928,0.00007802418,0.00008050266,0.0003839568,0.0004880848,0.00007069206,0.000001575664],"category_scores_gemma":[0.0004425829,0.0001248288,0.00009528848,0.0001502328,0.00002647826,0.002376235,0.0004664243,0.000191917,0.000001468138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001627236,"about_ca_system_score_gemma":0.0003418629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.919194e-7,"about_ca_topic_score_gemma":5.432602e-7,"domain_scores_codex":[0.9983326,0.0000201051,0.0007379124,0.00009279689,0.0006088712,0.0002076693],"domain_scores_gemma":[0.9979306,0.0005353052,0.0005963655,0.0001979026,0.0004980435,0.0002417817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002913265,0.0003613319,0.0005139073,0.001660829,0.0002189068,0.00008287924,0.04144129,0.03904526,0.001339653,0.01995102,0.02478084,0.8703128],"study_design_scores_gemma":[0.002452117,0.0003754598,0.0007431518,0.0003889885,0.00004030706,0.0007683836,0.001347649,0.9033938,0.005487935,0.01558323,0.06904548,0.0003734965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1694345,0.0002053181,0.8280441,0.0005482189,0.0004097259,0.0001630366,0.000004071054,0.00001827631,0.001172712],"genre_scores_gemma":[0.136409,0.00003599432,0.8629053,0.0003977967,0.0001925519,0.000003048597,0.000002420663,0.000009617483,0.00004436194],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8699393,"threshold_uncertainty_score":0.5090369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06401385121419086,"score_gpt":0.3221815610132692,"score_spread":0.2581677097990784,"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."}}