{"id":"W4407291405","doi":"10.1007/s41666-025-00189-6","title":"A Guided Variational Autoencoder for Targeted Molecule Optimization in Drug Discovery","year":2025,"lang":"en","type":"article","venue":"Journal of Healthcare Informatics Research","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Autoencoder; Drug discovery; Drug; Medicine; Computer science; Pharmacology; Mathematics; Artificial intelligence; Bioinformatics; Biology; Artificial neural network","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":[0.001302772,0.0007971658,0.001272045,0.0004686478,0.0003903255,0.0005679265,0.001603898,0.00165939,0.001744645],"category_scores_gemma":[0.002242109,0.0007910824,0.0007709126,0.0004448432,0.0006879213,0.0007571991,0.001330784,0.001405056,0.0004220752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008053957,"about_ca_system_score_gemma":0.001678922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01261762,"about_ca_topic_score_gemma":0.01304139,"domain_scores_codex":[0.9996859,0.0001271046,0.00001900589,0.0000474754,0.00007693153,0.00004356878],"domain_scores_gemma":[0.9991911,0.0005369016,0.00004286248,0.00004840396,0.0001444477,0.00003621751],"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":[0.00005347819,0.00003302081,0.0001688763,0.00003602621,0.00004714085,0.00002680679,0.00002097878,0.9496189,0.001477579,0.00478868,0.0009011957,0.04282735],"study_design_scores_gemma":[0.000002223132,0.000006048525,0.00001051917,0.000001453985,0.000002197523,0.000002203296,8.725069e-7,0.9993207,0.000119746,0.0004650178,0.00006794403,0.000001003831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01481458,0.0004708305,0.9825852,0.000223503,0.00006871596,0.00004387717,0.00004873193,0.0003898579,0.001354628],"genre_scores_gemma":[0.5827679,0.0005232368,0.4075034,0.0005857938,0.0001274116,0.0003070178,0.0003004743,0.0002444039,0.007640387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01261762,"threshold_uncertainty_score":0.02508837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0713148894159887,"score_gpt":0.451245856058421,"score_spread":0.3799309666424324,"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."}}