{"id":"W2751518520","doi":"10.3390/molecules22091375","title":"MTLD, a Database of Multiple Target Ligands, the Updated Version","year":2017,"lang":"en","type":"article","venue":"Molecules","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Jewish General Hospital","funders":"Fundamental Research Funds for the Central Universities","keywords":"Database; Computer science; Drug discovery; Bioinformatics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001385231,0.001502371,0.002106728,0.004309682,0.0006125508,0.00368471,0.002957596,0.001851475,0.05626627],"category_scores_gemma":[0.00636893,0.0009670592,0.00113311,0.005666618,0.0003709085,0.002950535,0.001890738,0.002088272,0.03005446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009740304,"about_ca_system_score_gemma":0.004709427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004196993,"about_ca_topic_score_gemma":0.005112533,"domain_scores_codex":[0.9989197,0.0002094048,0.0002367528,0.0001691605,0.000365757,0.00009917027],"domain_scores_gemma":[0.9982588,0.0005227676,0.0002836737,0.0002941279,0.0003720817,0.0002684893],"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":[0.001657026,0.0003027815,0.004749252,0.006167825,0.0003859583,0.0008082102,0.0001536154,0.006329929,0.01469729,0.01361648,0.6911074,0.2600242],"study_design_scores_gemma":[0.0004128624,0.0001728942,0.001909978,0.0002291365,0.0001845519,0.0006700344,0.00005216191,0.004972715,0.004736437,0.004338007,0.9822311,0.00009023107],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01169259,0.01679028,0.05611589,0.00189818,0.0006567095,0.0004948172,0.8375316,0.04298985,0.03183001],"genre_scores_gemma":[0.02412672,0.007825439,0.05122027,0.001157368,0.0001497247,0.0006167415,0.9000475,0.002280161,0.01257615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05626627,"threshold_uncertainty_score":0.1882294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02234655057547916,"score_gpt":0.2938847543994701,"score_spread":0.2715382038239909,"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."}}