{"id":"W2972365351","doi":"10.1007/978-3-030-30493-5_66","title":"Progressive Docking - Deep Learning Based Approach for Accelerated Virtual Screening","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Docking (animal); Computer science; Virtual screening; Artificial intelligence; Machine learning; Simulation; Chemistry; Molecular dynamics; Computational chemistry","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":[0.0006791406,0.0008757823,0.0008377807,0.00060631,0.0002312616,0.0005538806,0.001847583,0.0006687231,0.006440616],"category_scores_gemma":[0.0008995832,0.0004269693,0.0006426402,0.0006940779,0.000462243,0.0006640542,0.00156223,0.001437446,0.00176116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000773641,"about_ca_system_score_gemma":0.0008535302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003210999,"about_ca_topic_score_gemma":0.003230093,"domain_scores_codex":[0.9996181,0.00008352518,0.00001672065,0.00006250559,0.0001641225,0.00005514447],"domain_scores_gemma":[0.9996693,0.0001171334,0.00002686998,0.00007174874,0.00008180636,0.00003324081],"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":[0.0001992828,0.0001757592,0.0004798264,0.000162899,0.00007036339,0.0001332576,0.00003135375,0.6728584,0.02725249,0.01332305,0.004336927,0.2809764],"study_design_scores_gemma":[0.0000139703,0.00003753904,0.00009291453,0.000004292176,0.000005726709,0.00002615135,0.000001750223,0.9928392,0.003536105,0.002124152,0.001312284,0.000006033205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02207384,0.0005177216,0.9678132,0.0001946905,0.00006750801,0.00009717037,0.0001617053,0.004235677,0.004838433],"genre_scores_gemma":[0.4140134,0.0006306832,0.5742357,0.0003243659,0.00005111977,0.0002341904,0.0006787254,0.000394108,0.009437549],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006440616,"threshold_uncertainty_score":0.02154601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04062290999143017,"score_gpt":0.3043528758643381,"score_spread":0.2637299658729079,"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."}}