{"id":"W4220956251","doi":"10.1002/cmdc.202200092","title":"Jumping from Fragment to Drug via Smart Scaffolds","year":2022,"lang":"en","type":"article","venue":"ChemMedChem","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Mitacs","keywords":"Fragment (logic); Jumping; Drug; Drug discovery; Computational biology; Chemistry; Combinatorial chemistry; Computer science; Pharmacology; Medicine; Biology; Biochemistry; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002681419,0.0005892549,0.000642028,0.0005927049,0.0002231884,0.0007066375,0.0006203517,0.0004170901,0.004546865],"category_scores_gemma":[0.0004775092,0.0002964722,0.0005077794,0.0003847078,0.0004612412,0.0005757262,0.0009640225,0.0006459302,0.00190778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004113845,"about_ca_system_score_gemma":0.0003839929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003748235,"about_ca_topic_score_gemma":0.000640004,"domain_scores_codex":[0.9998024,0.00002777727,0.00001391763,0.00005352331,0.00006819762,0.00003418887],"domain_scores_gemma":[0.9998221,0.00005723999,0.00003890977,0.00003964671,0.00001655905,0.00002558198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008035367,0.0004152374,0.0009160877,0.0005753351,0.0001089638,0.0009741552,0.0001192697,0.0414002,0.8116224,0.01894885,0.003110036,0.121006],"study_design_scores_gemma":[0.0004180967,0.003673976,0.001000916,0.00006113201,0.0001406445,0.001072542,0.00008030381,0.1050429,0.819619,0.008527801,0.06023441,0.0001283145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6145295,0.003326835,0.3407171,0.0009356881,0.0002980612,0.001302075,0.002030052,0.005548524,0.0313121],"genre_scores_gemma":[0.8276482,0.002232693,0.1595583,0.000510973,0.00004879516,0.0004998161,0.001249143,0.0002407444,0.008011305],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004546865,"threshold_uncertainty_score":0.01521075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01678949077814928,"score_gpt":0.2648119969746347,"score_spread":0.2480225061964854,"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."}}