{"id":"W4417269368","doi":"10.1038/s42256-025-01154-z","title":"Deciphering RNA–ligand binding specificity with GerNA-Bind","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Virtual screening; MALAT1; Benchmark (surveying); RNA; Small molecule; Drug discovery; Deep learning; Binding site","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":[0.0002381655,0.0002256555,0.0001757561,0.00007579773,0.0001385181,0.00005906224,0.0004071339,0.0003419011,0.00005709378],"category_scores_gemma":[0.0001335471,0.0001786042,0.00007502436,0.0002375036,0.00005962399,0.000005323948,0.0001326007,0.0003875881,0.00001909862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002218591,"about_ca_system_score_gemma":0.000059277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002479525,"about_ca_topic_score_gemma":0.00007202173,"domain_scores_codex":[0.9988402,0.00004146374,0.0002017749,0.0004663756,0.0001777237,0.0002724482],"domain_scores_gemma":[0.9993215,0.00003558854,0.00007780851,0.000420391,0.00007884339,0.00006586259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002817684,0.0000386444,0.001548258,0.00002830432,0.00007691151,0.00001719493,0.00002347833,0.0001253908,0.9509269,0.002327547,0.0002783912,0.04432722],"study_design_scores_gemma":[0.00009226938,0.0001327059,0.0002150346,0.00008579726,0.00001684785,0.00001656688,0.00003707598,0.00007452475,0.9757999,0.0007498813,0.02256646,0.000212947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6816828,0.004895688,0.2969843,0.0003453769,0.0005364747,0.0003213448,0.00001916295,0.00004100852,0.01517385],"genre_scores_gemma":[0.9912291,0.0002826131,0.005052725,0.0003236711,0.0001657963,0.00001659151,0.00002075427,0.00002258244,0.002886129],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3095464,"threshold_uncertainty_score":0.7283267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006392768097142391,"score_gpt":0.2615052933426644,"score_spread":0.255112525245522,"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."}}