{"id":"W4409362560","doi":"10.1609/aaai.v39i24.34804","title":"BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Reinforcement learning; Computer science; Scalability; Reinforcement; Human–computer interaction; Cognitive science; Natural language processing; Artificial intelligence; Psychology; Social psychology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001656331,0.0002394351,0.0003196844,0.0001390898,0.0004026433,0.0003542359,0.0008816136,0.0001207469,0.0001768509],"category_scores_gemma":[0.002136843,0.0001865841,0.00006728873,0.0003981533,0.0002857175,0.0002081987,0.0003387414,0.0002894531,0.00003268498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000469009,"about_ca_system_score_gemma":0.00009147575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008797011,"about_ca_topic_score_gemma":0.000001550799,"domain_scores_codex":[0.9980226,0.00004749798,0.0005555146,0.0005474135,0.0003903116,0.0004366713],"domain_scores_gemma":[0.9987657,0.000230034,0.000321289,0.0002173086,0.0003965104,0.00006916128],"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":[0.000135036,0.00002896222,0.00002697243,0.0001091149,0.000005403409,2.113258e-7,0.0007501288,0.0805196,0.7030053,0.2105197,0.000008116999,0.004891466],"study_design_scores_gemma":[0.00001935699,0.0000970187,0.000002545703,0.000275519,0.00001373521,8.171025e-7,0.000291312,0.4714452,0.4557358,0.07200533,0.000008848059,0.0001044714],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2574595,0.00003813713,0.739817,0.0007045798,0.0002751629,0.0005768549,0.000001588005,0.00006977119,0.0010575],"genre_scores_gemma":[0.8997851,0.00001420138,0.09959463,0.0002070177,0.00003214456,0.00008431807,4.693429e-7,0.00001560935,0.0002665329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6423256,"threshold_uncertainty_score":0.7608678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04331414351777456,"score_gpt":0.3168851664140138,"score_spread":0.2735710228962392,"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."}}