{"id":"W2890418198","doi":"10.1371/journal.pone.0200769","title":"Virtual screening using covalent docking to find activators for G245S mutant p53","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Allard Foundation","keywords":"In silico; Virtual screening; Mutant; Docking (animal); Activator (genetics); Small molecule; Computational biology; Chemistry; Drug discovery; Database search engine; Biology; Biochemistry; Gene; Medicine; Computer science; Search engine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006505143,0.001434722,0.001546528,0.001041983,0.0003280393,0.000875977,0.001094953,0.0004583629,0.002829092],"category_scores_gemma":[0.0009061542,0.0003535787,0.001129828,0.001542476,0.0003246203,0.0003096443,0.0008144469,0.0005062015,0.0004715601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000539557,"about_ca_system_score_gemma":0.0006196074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002337715,"about_ca_topic_score_gemma":0.002202863,"domain_scores_codex":[0.9996483,0.0001365702,0.00001719059,0.00005143142,0.00009773652,0.00004878051],"domain_scores_gemma":[0.9997911,0.000118304,0.00002499982,0.00002082626,0.00001801907,0.00002672326],"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":[0.002136746,0.0006088899,0.004024106,0.0009217323,0.0008320148,0.0004826129,0.00005948005,0.9034663,0.04258849,0.004491115,0.003384662,0.03700392],"study_design_scores_gemma":[0.0007514107,0.002009796,0.002430879,0.0000484123,0.0004268577,0.0002448082,0.00005557829,0.9468642,0.03899741,0.002145179,0.005946501,0.00007905567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9248028,0.003336785,0.04786294,0.0003690029,0.00009568217,0.0005935318,0.004089274,0.003542804,0.01530709],"genre_scores_gemma":[0.9674576,0.001828541,0.02543945,0.0001144543,0.00001505841,0.0004395548,0.00273988,0.000119854,0.001845458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002829092,"threshold_uncertainty_score":0.009464324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1593098393879741,"score_gpt":0.3385038169202111,"score_spread":0.179193977532237,"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."}}