{"id":"W2089879448","doi":"10.1038/nchem.1954","title":"Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery","year":2014,"lang":"en","type":"article","venue":"Nature Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; National Institutes of Health","keywords":"Chemistry; Docking (animal); Conformational ensembles; Protein–ligand docking; Ligand (biochemistry); Searching the conformational space for docking; Protein Data Bank; Drug discovery; Flexibility (engineering); Stereochemistry; Protein structure; Computational biology; Crystallography; Combinatorial chemistry; Computational chemistry; Molecular dynamics; Receptor; Virtual screening; Biochemistry","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":[0.0005508896,0.0001115488,0.0001565784,0.00005651793,0.00003630366,0.00009913809,0.0002760655,0.0001352985,0.000001438135],"category_scores_gemma":[0.0003801216,0.0001071705,0.00002964981,0.0002407104,0.00004657415,0.0006713888,0.0002439078,0.0002204563,3.156874e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005167793,"about_ca_system_score_gemma":0.0001216255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002824058,"about_ca_topic_score_gemma":0.00001331191,"domain_scores_codex":[0.998967,0.00004884683,0.0002759572,0.0002900543,0.0002964504,0.000121683],"domain_scores_gemma":[0.9992333,0.0001864801,0.0001440895,0.0002575431,0.0001247812,0.00005379029],"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":[0.0001473632,0.0001547719,0.003098227,0.0006510805,0.00002694253,0.000003138493,0.0008153201,0.02448638,0.6293249,0.2866292,0.00009661877,0.05456608],"study_design_scores_gemma":[0.0004122087,0.00003408496,0.01053618,0.0001088793,0.00000230185,0.000006373633,0.00002963038,0.1303412,0.7998452,0.0581509,0.0003244681,0.0002085455],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7483974,0.00007140488,0.2500425,0.0002770539,0.00004936877,0.00009427574,0.000009424084,0.00001866564,0.001039939],"genre_scores_gemma":[0.9791271,9.201992e-7,0.02054009,0.000157527,0.00006648571,0.00001560476,0.00001738257,0.000004002665,0.00007085293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2307297,"threshold_uncertainty_score":0.4370288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006691873811595436,"score_gpt":0.2587905498481516,"score_spread":0.2520986760365562,"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."}}