{"id":"W4414380127","doi":"10.1093/bioinformatics/btaf501","title":"ChemBounce: a computational framework for scaffold hopping in drug discovery","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Research Foundation of Korea; Korea Research Institute of Chemical Technology","keywords":"Drug discovery; Source code; Scaffold; Code (set theory); Software","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":[0.0006986447,0.0001625646,0.0002237975,0.0003361927,0.0001226652,0.0004642332,0.0007375297,0.00007105536,0.000001565709],"category_scores_gemma":[0.0003744067,0.0001637628,0.0001043563,0.0009296482,0.0000480566,0.001543849,0.0003258613,0.0001416632,0.00001220503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001673911,"about_ca_system_score_gemma":0.0004575793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008906056,"about_ca_topic_score_gemma":0.000005289464,"domain_scores_codex":[0.9985819,0.00004175929,0.0005533745,0.0002155011,0.0003119747,0.0002955293],"domain_scores_gemma":[0.9977451,0.001620694,0.0001463096,0.0003325234,0.0001110444,0.00004433245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000929018,0.00004638004,0.0002361834,0.0001516434,0.00001679145,7.7089e-7,0.0009838393,0.120375,0.000002684274,0.8568785,0.001322139,0.01997677],"study_design_scores_gemma":[0.0003037753,0.00001036496,0.001829935,0.0001627283,0.000003216974,0.000001553603,0.0001045725,0.672196,0.00008205931,0.3238476,0.001327512,0.0001306953],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0109265,0.00008306817,0.9840589,0.00133062,0.0006170857,0.0004104413,0.00001697887,0.00009596938,0.002460411],"genre_scores_gemma":[0.107451,0.000004112254,0.8908532,0.001379529,0.00003879601,0.00005620205,0.00002298369,0.000006070167,0.0001881651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.551821,"threshold_uncertainty_score":0.6678054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967541515472338,"score_gpt":0.3165883645346435,"score_spread":0.2969129493799201,"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."}}