{"id":"W4394543423","doi":"10.6084/m9.figshare.22188034","title":"An approach combining deep learning and molecule docking for drug discovery of cathepsin L","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Ottawa","funders":"","keywords":"Drug discovery; Docking (animal); Computational biology; Computer science; Chemistry; Artificial intelligence; Biology; Biochemistry; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001502338,0.0002724396,0.0003000762,0.00008928788,0.0001214367,0.0001021785,0.000374118,0.0003061299,0.0001491879],"category_scores_gemma":[0.001196085,0.000274382,0.0001045888,0.00008312241,0.00001878571,0.00001085014,0.0003573742,0.0003425304,0.00003518445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009753066,"about_ca_system_score_gemma":0.00005426444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000342568,"about_ca_topic_score_gemma":0.00002212954,"domain_scores_codex":[0.9988238,0.0000729849,0.0003092021,0.0003709885,0.0001573703,0.0002656439],"domain_scores_gemma":[0.9989761,0.00007736753,0.000389158,0.0004092955,0.00008464819,0.00006340139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002089515,0.00003090793,0.00001451623,0.001874308,0.00005893282,0.000001252527,0.00006901351,0.0008902806,0.0003625042,0.000001371111,0.9964122,0.0002637743],"study_design_scores_gemma":[0.0004958874,0.0002998136,0.0000526285,0.0008675873,0.00005780713,0.0000141009,0.0002508526,0.009700613,0.001293072,0.000006771512,0.9864187,0.0005421612],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003283824,0.000345596,0.00006217357,0.000003700643,0.00004569962,0.0003196371,0.9987757,0.00002383381,0.00009529573],"genre_scores_gemma":[0.001086415,0.00004509473,0.0007088781,0.00004012346,0.0001518469,0.0001561723,0.9975818,0.00005653979,0.0001730761],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009993539,"threshold_uncertainty_score":0.9999709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470448301920448,"score_gpt":0.2860187244589894,"score_spread":0.2713142414397849,"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."}}