{"id":"W4408461854","doi":"10.1021/acs.jcim.4c02309","title":"Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein–Ligand Binding Affinity Prediction","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China; Ministry of Education - Singapore","keywords":"Metalloprotein; Join (topology); Homology (biology); Computer science; Computational biology; Ligand (biochemistry); Signal transducing adaptor protein; Chemistry; Artificial intelligence; Biology; Biochemistry; Receptor; Gene; Mathematics; Enzyme","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":[0.001361834,0.0005364466,0.001372962,0.001972379,0.0005125395,0.001056032,0.001591938,0.001097177,0.001782872],"category_scores_gemma":[0.003389239,0.0003360158,0.001202478,0.00151522,0.000962832,0.001992763,0.001768156,0.001520505,0.0004882007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009322371,"about_ca_system_score_gemma":0.001172942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003105188,"about_ca_topic_score_gemma":0.002020039,"domain_scores_codex":[0.9993924,0.0001839945,0.00003330943,0.0001081075,0.0002152614,0.00006681857],"domain_scores_gemma":[0.9986541,0.0007242035,0.0001595619,0.0001824583,0.0001825413,0.00009720359],"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":[0.0001069323,0.0001114681,0.003671241,0.0001330062,0.00008454414,0.00009464395,0.00006755672,0.8512099,0.00256336,0.03909234,0.002620713,0.1002443],"study_design_scores_gemma":[0.000003561725,0.0000211346,0.0001205421,0.000002518075,0.000003780158,0.0000140537,0.00000433613,0.9890961,0.0002954424,0.01010335,0.0003309064,0.000004352358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05039341,0.0007749945,0.9458779,0.0003163494,0.00003758905,0.0000746748,0.0003216711,0.0007779728,0.001425503],"genre_scores_gemma":[0.8176402,0.0007695612,0.1769644,0.0002894908,0.0001079095,0.0002871378,0.001429511,0.0001795323,0.002332358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003105188,"threshold_uncertainty_score":0.007202148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03287065993465929,"score_gpt":0.2913620704193207,"score_spread":0.2584914104846614,"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."}}