{"id":"W4414510082","doi":"10.21203/rs.3.rs-7540516/v1","title":"BiRLNN: Bidirectional Reinforcement-Learning Neural Network for Constrained Molecular Design","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Defence Research and Development Canada; National Research Council Canada","funders":"","keywords":"Reinforcement learning; Process (computing); Constraint (computer-aided design); Space (punctuation); Artificial neural network; Chemical space; Function (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008093489,0.0006847839,0.0007253402,0.0003283466,0.0003532715,0.000505663,0.001559225,0.001348534,0.008355123],"category_scores_gemma":[0.002186353,0.0003535522,0.000344705,0.0004373865,0.0005438495,0.0008157647,0.001365131,0.001736295,0.0016988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007576058,"about_ca_system_score_gemma":0.0009553081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004005782,"about_ca_topic_score_gemma":0.005968439,"domain_scores_codex":[0.9997856,0.00006968919,0.000008070818,0.00004546757,0.00006677833,0.00002444403],"domain_scores_gemma":[0.9995913,0.000184701,0.00003537603,0.00005722568,0.00009250156,0.00003893862],"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":[0.0001646827,0.0001096573,0.00039666,0.0001969464,0.00004413023,0.0000534853,0.0000295975,0.7925609,0.00398308,0.03305801,0.01204902,0.1573538],"study_design_scores_gemma":[0.000009503281,0.00001132734,0.00002069162,0.000004551278,0.000002241555,0.000003638159,0.000001172549,0.9933465,0.0004792282,0.005263039,0.0008556894,0.000002486073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01070616,0.0006045849,0.9778609,0.0003810849,0.0001995756,0.00006720956,0.0002672773,0.002362355,0.007550818],"genre_scores_gemma":[0.4458687,0.0004964194,0.5344902,0.0005773908,0.00009911528,0.0006112515,0.0009935942,0.0009031903,0.01596007],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008355123,"threshold_uncertainty_score":0.02795064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06354384895276662,"score_gpt":0.3933398122354619,"score_spread":0.3297959632826953,"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."}}