{"id":"W4406795606","doi":"10.1021/acscatal.4c07972","title":"Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan–Lam Coupling of Primary Sulfonamides","year":2025,"lang":"en","type":"article","venue":"ACS Catalysis","topic":"Sulfur-Based Synthesis Techniques","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"Office of Research Infrastructure Programs, National Institutes of Health; Division of Chemistry; National Institutes of Health; National Science Foundation","keywords":"Selection (genetic algorithm); Coupling (piping); Drug discovery; Primary (astronomy); Combinatorial chemistry; Computer science; Chemistry; Computational biology; Biochemical engineering; Physics; Biology; Bioinformatics; Artificial intelligence; Materials science; Engineering","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":[0.004723337,0.001516591,0.001395989,0.00115944,0.0004069744,0.001631388,0.001230815,0.001250957,0.001452914],"category_scores_gemma":[0.009810749,0.0006730779,0.001762192,0.0005462041,0.0009673301,0.0009167015,0.0005952474,0.001981722,0.0003382725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001866445,"about_ca_system_score_gemma":0.002421544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009415542,"about_ca_topic_score_gemma":0.008172833,"domain_scores_codex":[0.999186,0.000293012,0.0000610475,0.0002522472,0.0001132554,0.00009434693],"domain_scores_gemma":[0.9923213,0.00612199,0.0005519705,0.0002361054,0.0006322582,0.0001363446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001526488,0.0001739639,0.006560849,0.0001275179,0.00008132322,0.00005420317,0.00003916776,0.9817896,0.002196637,0.001851142,0.0006313386,0.006341515],"study_design_scores_gemma":[0.000013119,0.0000306331,0.0004544716,0.000005644559,0.00001259521,0.000005725022,0.000006953183,0.9973308,0.001177274,0.0008136894,0.0001419113,0.000007169906],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7035912,0.001051623,0.2834085,0.001223288,0.00006050613,0.0003911491,0.005940345,0.001477044,0.002856436],"genre_scores_gemma":[0.9379689,0.0002782294,0.05470537,0.0001791524,0.00002780293,0.0005401659,0.005348375,0.00007402569,0.000878056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009415542,"threshold_uncertainty_score":0.02497971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0325520516640259,"score_gpt":0.3097992267332825,"score_spread":0.2772471750692566,"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."}}