{"id":"W2324869514","doi":"10.1021/cb5001636","title":"Increasing Chemical Space Coverage by Combining Empirical and Computational Fragment Screens","year":2014,"lang":"en","type":"article","venue":"ACS Chemical Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Chemotype; Fragment (logic); Chemical space; Virtual screening; Surface plasmon resonance; Computational biology; Chemistry; Stereochemistry; Drug discovery; Biology; Computer science; Nanotechnology; Biochemistry; Algorithm; Materials science; Chromatography","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":[0.002319198,0.001519845,0.001472155,0.00192361,0.0003801318,0.001443687,0.001811481,0.0006934868,0.003132534],"category_scores_gemma":[0.006637434,0.0005672557,0.001125171,0.001335363,0.0006242114,0.001521091,0.001866304,0.0008374689,0.0005553905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009219202,"about_ca_system_score_gemma":0.001485746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002024342,"about_ca_topic_score_gemma":0.003064903,"domain_scores_codex":[0.9987697,0.0004076555,0.00008374202,0.0001686387,0.0004332908,0.0001370678],"domain_scores_gemma":[0.9969414,0.001826866,0.0002126177,0.0005114569,0.0004093173,0.000098304],"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":[0.001018969,0.0007003898,0.01048536,0.000803438,0.0003321863,0.000374495,0.0001299333,0.7755128,0.05998936,0.01199925,0.00298083,0.135673],"study_design_scores_gemma":[0.0001034384,0.0005839674,0.0008455881,0.00003156419,0.0001365946,0.0001235239,0.00004459745,0.9765202,0.01546717,0.003209775,0.002898828,0.00003483982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.667711,0.002201288,0.3012512,0.0007908792,0.00009712319,0.0005699811,0.001698173,0.007028305,0.01865205],"genre_scores_gemma":[0.8277885,0.001267975,0.1659355,0.0003330951,0.00004557252,0.0004706125,0.002319837,0.0004629669,0.001375794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003132534,"threshold_uncertainty_score":0.01226526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01845287974029771,"score_gpt":0.3049016917448281,"score_spread":0.2864488120045304,"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."}}