{"id":"W3129051420","doi":"10.1021/acs.jcim.0c01097","title":"Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures","year":2021,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Invariant (physics); Artificial intelligence; Deep learning; Pattern recognition (psychology); Machine learning; Biological system; Mathematics; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0004701182,0.00008418019,0.0001388466,0.0001400159,0.00006087304,0.0002812443,0.000193902,0.00003228016,7.061437e-7],"category_scores_gemma":[0.0002505337,0.00007470655,0.00003703036,0.0003031539,0.000007094707,0.0009320234,0.0001368966,0.0001889046,0.000002136613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004637903,"about_ca_system_score_gemma":0.0001257275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000214527,"about_ca_topic_score_gemma":1.246789e-7,"domain_scores_codex":[0.9989265,0.00004361479,0.000424895,0.00010914,0.0003862692,0.0001095534],"domain_scores_gemma":[0.9988729,0.00008778633,0.0001763884,0.00008670841,0.0006338406,0.0001423611],"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":[0.00001478796,0.00001631896,0.000007986355,0.00001846852,0.00001323013,0.000001872734,0.002057622,0.9512765,0.01930119,0.009704933,0.000005032245,0.01758209],"study_design_scores_gemma":[0.0002088964,0.00002025155,0.00002950636,0.00003754378,0.000005398148,0.0002305271,0.0001845161,0.9869986,0.01025626,0.00172272,0.0002064468,0.00009934697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.282969,0.00002943209,0.7158005,0.0006574063,0.00001706042,0.00005483921,1.988222e-7,0.00001081754,0.000460776],"genre_scores_gemma":[0.5775478,0.000001500526,0.4215516,0.0008682811,0.00001969566,0.000003066743,0.000004807976,0.000002408214,7.679172e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2945789,"threshold_uncertainty_score":0.3046445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01441850528420703,"score_gpt":0.2678377676925525,"score_spread":0.2534192624083454,"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."}}