{"id":"W2911947907","doi":"10.1016/j.ejmech.2019.02.046","title":"Challenges and current status of computational methods for docking small molecules to nucleic acids","year":2019,"lang":"en","type":"review","venue":"European Journal of Medicinal Chemistry","topic":"DNA and Nucleic Acid Chemistry","field":"Biochemistry, Genetics and Molecular Biology","cited_by":95,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Genome Canada","keywords":"Nucleic acid; Docking (animal); Chemistry; In silico; Small molecule; Computational biology; Drug discovery; RNA; Nucleic acid structure; Biochemistry; Biology","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":[0.003192363,0.00170415,0.002894219,0.001198671,0.0004081577,0.002264916,0.003832849,0.001608694,0.003385105],"category_scores_gemma":[0.004858249,0.0006525142,0.001284194,0.002193303,0.001307784,0.002266631,0.001805364,0.002987267,0.00183587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007419367,"about_ca_system_score_gemma":0.002142832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003544105,"about_ca_topic_score_gemma":0.002917712,"domain_scores_codex":[0.9991122,0.0003052513,0.00007546269,0.0001402726,0.0003033002,0.00006336591],"domain_scores_gemma":[0.9968112,0.002338406,0.0001258738,0.0001268779,0.0004987797,0.00009894952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000197271,0.000173305,0.0004172151,0.01283748,0.0005079519,0.00007714221,0.00006719204,0.01952761,0.001546456,0.02567332,0.02202998,0.916945],"study_design_scores_gemma":[0.0003038057,0.0004858925,0.00116442,0.01042809,0.001009061,0.0008783102,0.0002402123,0.06808259,0.005429005,0.07117807,0.8405654,0.0002349953],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009053081,0.9813702,0.01418843,0.001197957,0.0004149693,0.00001847255,0.00009223462,0.0001397931,0.001672578],"genre_scores_gemma":[0.005937041,0.9734839,0.01818019,0.0005774974,0.0005524983,0.00005828101,0.0002870879,0.00005622369,0.0008672454],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003832849,"threshold_uncertainty_score":0.01688302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08951594543874189,"score_gpt":0.3867281771411668,"score_spread":0.2972122317024249,"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."}}