{"id":"W2519263923","doi":"10.1021/acs.jmedchem.6b00355","title":"Discovery and Optimization of a Selective Ligand for the Switch/Sucrose Nonfermenting-Related Bromodomains of Polybromo Protein-1 by the Use of Virtual Screening and Hydration Analysis","year":2016,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Structural Genomics Consortium; Seventh Framework Programme; Else Kröner-Fresenius-Stiftung; Wellcome Trust; European Cooperation in Science and Technology; Cancer Research UK; Wellcome","keywords":"Isothermal titration calorimetry; Bromodomain; Chemistry; Ligand (biochemistry); Virtual screening; Computational biology; Combinatorial chemistry; Protein–protein interaction; Drug discovery; Ligand efficiency; Drug design; Epigenetics; Biophysics; Biochemistry; Biology; Receptor; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001757511,0.0003506722,0.000457845,0.0002018207,0.000161043,0.0002998953,0.0003596463,0.0001783965,0.0009909794],"category_scores_gemma":[0.0001567312,0.0001179462,0.0002477447,0.0001893092,0.0001681673,0.0001777056,0.0003069504,0.0004526538,0.0001993954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003035465,"about_ca_system_score_gemma":0.0002613677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005112401,"about_ca_topic_score_gemma":0.001293255,"domain_scores_codex":[0.9999365,0.00001219993,0.000003410085,0.00001393207,0.00002147468,0.00001246778],"domain_scores_gemma":[0.9999666,0.000007107888,0.000009050962,0.000002477673,0.000005704212,0.000009010037],"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":[0.0008205646,0.000454632,0.0009671514,0.0003520415,0.00006688348,0.0002565579,0.00006480832,0.01980851,0.9315168,0.001229718,0.0004901761,0.04397223],"study_design_scores_gemma":[0.000492646,0.006966437,0.002393504,0.00003362199,0.0001855431,0.0009126337,0.00009815666,0.03745887,0.9388677,0.0004478373,0.01208606,0.00005685246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826061,0.003748487,0.01050608,0.0001274285,0.00002085447,0.0001724165,0.0003228333,0.0001452461,0.002350584],"genre_scores_gemma":[0.9872455,0.001902308,0.008928771,0.00005710344,0.000005832409,0.00006440702,0.0005662885,0.00001616988,0.001213577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009909794,"threshold_uncertainty_score":0.00331521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012798738766304,"score_gpt":0.2288826924852547,"score_spread":0.2187547050975917,"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."}}