{"id":"W2097166394","doi":"10.1177/1087057106294699","title":"Screening for Ligands Using a Generic and High-Throughput Light-Scattering-Based Assay","year":2006,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"thermodynamics and calorimetric analyses","field":"Chemistry","cited_by":129,"is_retracted":false,"has_abstract":false,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"","keywords":"Small molecule; Drug discovery; Computational biology; High-throughput screening; False positive paradox; Denaturation (fissile materials); Chemical genetics; Identification (biology); Protein stability; Homogeneous; Fluorescence; Structural genomics; Combinatorial chemistry; Protein–protein interaction; Functional genomics; Chemistry; Biological system; Genomics; Computer science; Protein structure; Biology; Biochemistry; Gene; Machine learning; Physics","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":[0.001836266,0.001716907,0.001852649,0.001708604,0.0008533099,0.001184286,0.00130146,0.001233919,0.001729584],"category_scores_gemma":[0.001297464,0.0005137221,0.0009228697,0.001674128,0.0007552685,0.0004928635,0.0009851992,0.001356972,0.001275719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009844191,"about_ca_system_score_gemma":0.0009942325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009736106,"about_ca_topic_score_gemma":0.003996317,"domain_scores_codex":[0.9980016,0.000505079,0.0001299107,0.00026059,0.0008394215,0.000263359],"domain_scores_gemma":[0.9993508,0.0001684891,0.00008788735,0.000102526,0.000170604,0.0001196401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002651989,0.0003045623,0.0006430334,0.0001364729,0.00005096332,0.00005356126,0.00002500524,0.00126541,0.9890633,0.0003546372,0.0005970633,0.007240801],"study_design_scores_gemma":[0.00008709379,0.001016212,0.001420109,0.00001040254,0.00006317774,0.0002526767,0.00002888452,0.004364352,0.9882697,0.0001905165,0.004257505,0.0000394458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.70803,0.003462505,0.26442,0.0009267203,0.0002716445,0.003179813,0.00617081,0.003134645,0.010404],"genre_scores_gemma":[0.8241752,0.002865607,0.147818,0.0007976253,0.0001020582,0.002114869,0.007934704,0.0002446507,0.01394732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001852649,"threshold_uncertainty_score":0.009711266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01975730054763946,"score_gpt":0.24750602544929,"score_spread":0.2277487249016505,"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."}}