{"id":"W2967672482","doi":"10.1101/738690","title":"Protein-protein docking using learned three-dimensional representations","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Macromolecular docking; Docking (animal); Protein structure prediction; Computer science; Protein structure; Decoy; Convolutional neural network; Artificial intelligence; Biological system; Pattern recognition (psychology); Algorithm; Chemistry; Biology; Biochemistry","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":[0.0006112541,0.001173774,0.001066978,0.0009510199,0.0003236981,0.001313573,0.001364387,0.001381743,0.002458118],"category_scores_gemma":[0.002102868,0.0004494062,0.0008404048,0.001199144,0.0005975401,0.001202191,0.001176417,0.001247169,0.0008163564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001728623,"about_ca_system_score_gemma":0.001092591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01454707,"about_ca_topic_score_gemma":0.01113983,"domain_scores_codex":[0.9996644,0.00008503164,0.00001699606,0.0000932828,0.00008984507,0.00005036844],"domain_scores_gemma":[0.9992799,0.0002485309,0.0000983082,0.0001650268,0.0001455757,0.00006269945],"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":[0.0001366983,0.0001080057,0.001135717,0.0000314202,0.00005358347,0.00004987869,0.00001284053,0.9593737,0.001899335,0.002590165,0.002191282,0.03241747],"study_design_scores_gemma":[0.000004493874,0.000005820926,0.00006346956,0.000001218909,0.000001077649,0.000003163648,0.000001282247,0.9988117,0.0002508838,0.0008090725,0.00004601424,0.000001790742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4199955,0.0008273176,0.5604814,0.0008673945,0.0001437995,0.0001250349,0.001894928,0.01064301,0.005021732],"genre_scores_gemma":[0.9218809,0.0001699124,0.07230263,0.0001465765,0.00002124497,0.00007995099,0.002799481,0.0001587554,0.002440599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01454707,"threshold_uncertainty_score":0.02892482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172766207476183,"score_gpt":0.2525021954735597,"score_spread":0.2352255747259414,"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."}}