{"id":"W4412784829","doi":"10.1021/acs.jcim.4c02041","title":"Transfer Learning for Heterocycle Retrosynthesis","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"Engineering and Physical Sciences Research Council; King Abdulaziz University; Alzheimer’s Research UK","keywords":"Retrosynthetic analysis; Computer science; Ring (chemistry); Drug discovery; Training set; Artificial intelligence; Machine learning; Synthetic data; Chemistry; Stereochemistry","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":[0.001875839,0.001210226,0.0008213166,0.0007400767,0.0003866109,0.0007438103,0.00136447,0.001365828,0.002846326],"category_scores_gemma":[0.003921579,0.0003548813,0.0009829107,0.0005758547,0.0005404376,0.001099161,0.000830278,0.001760204,0.0007973254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073908,"about_ca_system_score_gemma":0.001012229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005384621,"about_ca_topic_score_gemma":0.003899556,"domain_scores_codex":[0.9996194,0.0001402033,0.00002323305,0.000112278,0.00005491892,0.00004997178],"domain_scores_gemma":[0.9980711,0.00143689,0.0001214916,0.0001085383,0.0002010211,0.00006088182],"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":[0.0001565146,0.0001046555,0.0009649296,0.0000748752,0.00005410729,0.00004530751,0.00002142381,0.9335396,0.001406445,0.001272036,0.0008659502,0.06149418],"study_design_scores_gemma":[0.000004622369,0.00002229907,0.00006678621,0.000002286314,0.000003815894,0.000003364298,0.000002671958,0.9981762,0.000457615,0.001163627,0.00009377782,0.000002830511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2299926,0.002948557,0.7552701,0.00105483,0.0001869623,0.0002225823,0.0007795046,0.004608855,0.004935982],"genre_scores_gemma":[0.9206972,0.0003351097,0.07399621,0.0002903542,0.00007318585,0.0002706448,0.001037688,0.0001228143,0.003176741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005384621,"threshold_uncertainty_score":0.01070654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016185494338797,"score_gpt":0.2511769490775954,"score_spread":0.2410150941342074,"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."}}