{"id":"W4401589835","doi":"10.1186/s13321-024-00892-3","title":"Metis: a python-based user interface to collect expert feedback for generative chemistry models","year":2024,"lang":"en","type":"article","venue":"Journal of Cheminformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020; Engineering and Physical Sciences Research Council; Vetenskapsrådet; UK Research and Innovation; Knut och Alice Wallenbergs Stiftelse; Finnish Center for Artificial Intelligence; European Commission; Chalmers Tekniska Högskola","keywords":"Computer science; Python (programming language); Graphical user interface; Human–computer interaction; Interface (matter); User interface; Generative grammar; Generative model; Human-in-the-loop; Software engineering; Data science; Artificial intelligence; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007993894,0.0001860008,0.0003068756,0.0001536069,0.00005706991,0.0004810021,0.0007764824,0.00006743371,0.00001426713],"category_scores_gemma":[0.0002607592,0.0001549807,0.0002276442,0.0005051119,0.00002331769,0.001231416,0.0001430735,0.000201754,0.000007525407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585515,"about_ca_system_score_gemma":0.0007332294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.102147e-7,"about_ca_topic_score_gemma":1.678822e-7,"domain_scores_codex":[0.9983682,0.00001667523,0.0007528566,0.000137026,0.0004988766,0.0002263683],"domain_scores_gemma":[0.9980884,0.0007853872,0.0002423221,0.0002410497,0.0004645965,0.0001782726],"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":[0.00009545411,0.0000813775,4.313432e-7,0.0004903393,0.0002031833,0.00001546136,0.01325772,0.8366096,0.01196865,0.00244116,0.1097357,0.02510095],"study_design_scores_gemma":[0.0002788735,0.00007558076,5.076711e-7,0.0002210396,0.00001245746,0.0000607242,0.0001461122,0.7154567,0.2685973,0.002778125,0.01223389,0.0001386411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006965022,0.0004476238,0.9884219,0.001912694,0.000462383,0.0001985623,0.00001318697,0.00004312237,0.001535472],"genre_scores_gemma":[0.07819162,0.00001432424,0.9198524,0.0009854689,0.0002552878,0.0000277167,0.000002615178,0.00002128581,0.0006493187],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2566287,"threshold_uncertainty_score":0.6319929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03071467042314193,"score_gpt":0.3335950207731636,"score_spread":0.3028803503500216,"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."}}