{"id":"W4412454105","doi":"10.1093/bioinformatics/btaf259","title":"ADME-drug-likeness: enriching molecular foundation models via pharmacokinetics-guided multi-task learning for drug-likeness prediction","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Science and ICT, South Korea; Seoul National University; National Research Foundation; AIGENDRUG","keywords":"ADME; Computer science; Drug discovery; Drug development; Drug; Machine learning; Artificial intelligence; Computational biology; Pharmacology; Bioinformatics; Medicine; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001290081,0.000334535,0.0003242322,0.0004874576,0.0004391023,0.000491393,0.0008939736,0.00009480739,0.000002920068],"category_scores_gemma":[0.0002472034,0.0003556016,0.0001823495,0.0008876452,0.00005223403,0.002259304,0.0004533424,0.0002729635,0.00002119154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002368996,"about_ca_system_score_gemma":0.0003366691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002787021,"about_ca_topic_score_gemma":0.000003518691,"domain_scores_codex":[0.9974129,0.0002187463,0.0009641183,0.000407445,0.0005405561,0.0004562117],"domain_scores_gemma":[0.9979662,0.0005542216,0.0004015237,0.0004932483,0.0004695282,0.0001152967],"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":[0.00001569737,0.00007414851,0.00007279334,0.0003116701,0.00006838673,0.000001295129,0.001970996,0.8675829,0.001070829,0.01316505,0.0004615863,0.1152046],"study_design_scores_gemma":[0.00130748,0.00002475638,0.0001790375,0.0001097124,0.00005857831,0.000006702483,0.000146366,0.9784853,0.004045652,0.01091202,0.004404018,0.0003203666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01188344,0.0001349004,0.9837251,0.0003660871,0.001548564,0.0009189354,0.00001355122,0.0004193758,0.0009900257],"genre_scores_gemma":[0.3574541,0.00003922864,0.641097,0.0006016332,0.00008066293,0.0001646584,0.0001721613,0.00002767736,0.0003628361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3455707,"threshold_uncertainty_score":0.9998896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02793116672785135,"score_gpt":0.321388499425966,"score_spread":0.2934573326981146,"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."}}