{"id":"W1945063523","doi":"10.1002/ejoc.201300409","title":"A Modular Approach to Build Macrocyclic Diversity in Aminoindoline Scaffolds Identifies Antiangiogenesis Agents from a Zebrafish Assay","year":2013,"lang":"en","type":"article","venue":"European Journal of Organic Chemistry","topic":"Chemical Synthesis and Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Department of Science and Technology, Ministry of Science and Technology, India; Council of Scientific and Industrial Research, India; Department of Biotechnology, Ministry of Science and Technology, India","keywords":"Zebrafish; Chemistry; Moiety; Modular design; Image stitching; Chemical space; Scaffold; Combinatorial chemistry; Computational biology; Nanotechnology; Drug discovery; Stereochemistry; Biochemistry; Artificial intelligence; Biomedical engineering; Computer science; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004125831,0.0002140333,0.0003265402,0.00005155559,0.00007686065,0.000074468,0.000673997,0.00008174667,0.0005438368],"category_scores_gemma":[0.0001951412,0.0002000884,0.0002909894,0.0002283125,0.00004480285,0.0000137239,0.0006558069,0.0001130461,0.00004642578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004211559,"about_ca_system_score_gemma":0.0000241937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004323047,"about_ca_topic_score_gemma":0.000003741294,"domain_scores_codex":[0.9984595,0.0001314797,0.000505005,0.0003593226,0.0002809244,0.000263832],"domain_scores_gemma":[0.9989884,0.00001605865,0.0002616568,0.0003524944,0.0001281766,0.0002532294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002490396,0.0001880997,0.008691533,0.00001719216,0.0001851822,0.00003192841,0.00007654821,0.00002916566,0.9875463,5.673812e-8,0.002457743,0.0007513402],"study_design_scores_gemma":[0.0006743806,0.00002488582,0.04533675,0.00005365531,0.00009656194,0.0000408784,0.0001845373,0.00004019099,0.9514309,0.00001106254,0.001837969,0.0002682888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968375,0.0004208985,0.001225764,0.0001766207,0.00003952114,0.00006116593,0.00001606107,0.000006434837,0.001216041],"genre_scores_gemma":[0.9976819,0.00009009751,0.0009481561,0.0001919864,0.0004273394,9.906993e-7,0.00004299412,0.00003704868,0.0005795286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03664522,"threshold_uncertainty_score":0.815937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01071474934360106,"score_gpt":0.201897240341565,"score_spread":0.1911824909979639,"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."}}