{"id":"W4411211225","doi":"10.1021/acs.jcim.5c00497","title":"RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"U.S. National Library of Medicine; National Institute on Drug Abuse; NIH Office of the Director; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institutes of Health; Bundesministerium für Bildung und Forschung; Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases; National Institute on Aging; Deutsche Forschungsgemeinschaft; German Network for Bioinformatics Infrastructure; Deutscher Akademischer Austauschdienst; German Academic Exchange Service; National Institute of Allergy and Infectious Diseases; Alexander von Humboldt-Stiftung","keywords":"Monte Carlo method; Computer science; Algorithm; Combinatorial algorithms; Metropolis–Hastings algorithm; Markov chain Monte Carlo; Mathematics; Statistics","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":[0.001356154,0.0009963376,0.001314477,0.0006901116,0.0004746839,0.0009955501,0.002441361,0.00103661,0.00651527],"category_scores_gemma":[0.002347082,0.0009925517,0.001486286,0.0006704371,0.0005681507,0.0006740691,0.001110306,0.001957721,0.001647498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008729334,"about_ca_system_score_gemma":0.002271522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002305095,"about_ca_topic_score_gemma":0.003621417,"domain_scores_codex":[0.999423,0.0002415161,0.00002690244,0.00007486588,0.0001919777,0.00004170324],"domain_scores_gemma":[0.9992131,0.0004932865,0.00005642942,0.00009190207,0.00009890251,0.00004636201],"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":[0.000382429,0.0001790864,0.0007384586,0.0005215454,0.0003064437,0.0002302557,0.00009080464,0.7873424,0.02808799,0.04680362,0.00511927,0.1301976],"study_design_scores_gemma":[0.00006410252,0.00004447096,0.00002880064,0.000007268483,0.00001294829,0.00003152279,0.000003823473,0.9871869,0.004944855,0.004384296,0.003277675,0.00001333237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005476078,0.000167225,0.9883246,0.00005792974,0.0000247768,0.0001037436,0.0001598215,0.003718133,0.001967631],"genre_scores_gemma":[0.08093212,0.0002231676,0.9151641,0.0001228883,0.00001955078,0.000611416,0.0004323489,0.0009149871,0.001579472],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00651527,"threshold_uncertainty_score":0.02179575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02211340852123175,"score_gpt":0.2711968234538787,"score_spread":0.2490834149326469,"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."}}