{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001345723,0.0006184328,0.0009536575,0.0005720952,0.0007007512,0.001699539,0.001070599,0.001048794,0.004537165],"category_scores_gemma":[0.001854266,0.0006420776,0.0004909695,0.0003370487,0.0008451766,0.001525384,0.001125772,0.002072741,0.001316281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006880754,"about_ca_system_score_gemma":0.0003139359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005894503,"about_ca_topic_score_gemma":0.001850887,"domain_scores_codex":[0.9994543,0.0001041753,0.00002396211,0.0001818976,0.0001456053,0.00009000728],"domain_scores_gemma":[0.9994133,0.0002940429,0.00005421033,0.0001243561,0.00005126612,0.0000629661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00049848,0.00008143976,0.002494479,0.0002419536,0.00009569537,0.0001311415,0.0001555035,0.007211621,0.9524245,0.0130095,0.0006949553,0.02296078],"study_design_scores_gemma":[0.00002247767,0.00008820378,0.001180367,0.00001572769,0.00003670088,0.0002064032,0.00008331644,0.04471851,0.941903,0.006925086,0.004777216,0.00004299529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8495144,0.003854754,0.1344101,0.001308564,0.0002399832,0.00006008392,0.0004384586,0.002103623,0.008070034],"genre_scores_gemma":[0.9624944,0.0007295003,0.03209531,0.0005767604,0.00002910127,0.00002565299,0.0003968178,0.0002002371,0.003452297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004537165,"threshold_uncertainty_score":0.01517832,"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."}}