{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001884683,0.0008486944,0.0008381295,0.0009376326,0.0004172012,0.000827028,0.0007037089,0.0005881229,0.0007694822],"category_scores_gemma":[0.005472671,0.0004355502,0.0004957427,0.0006093268,0.0004383322,0.001173553,0.0008974141,0.000874536,0.0001549705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003682969,"about_ca_system_score_gemma":0.00062198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001080031,"about_ca_topic_score_gemma":0.002841477,"domain_scores_codex":[0.9994029,0.0002519843,0.00004962429,0.00006849685,0.0001676666,0.0000593584],"domain_scores_gemma":[0.9983131,0.0009743053,0.0002369593,0.0002398069,0.0001407162,0.00009516343],"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":[0.002190947,0.0008048062,0.01346795,0.0003001917,0.0003985944,0.0002340367,0.00007482866,0.7472513,0.1240342,0.005844287,0.0007495341,0.1046493],"study_design_scores_gemma":[0.00009993527,0.0003893696,0.001962546,0.00002375722,0.0001313222,0.00009372072,0.00001611482,0.9634726,0.03129781,0.002081738,0.0003926722,0.0000383452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8984161,0.0007981355,0.09570797,0.0003206911,0.00003970041,0.00008021622,0.0002587037,0.001459917,0.002918584],"genre_scores_gemma":[0.9742579,0.0001811539,0.02501417,0.00006996212,0.000007666595,0.00003112721,0.0001303184,0.00008657562,0.0002210822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001884683,"threshold_uncertainty_score":0.009967268,"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."}}