{"id":"W4410755510","doi":"10.1021/acs.jcim.5c00588","title":"Active Learning-Guided Hit Optimization for the Leucine-Rich Repeat Kinase 2 WDR Domain Based on In Silico Ligand-Binding Affinities","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Structural Genomics Consortium; University of Toronto; University Health Network; University of British Columbia; University of Ottawa","funders":"Division of Chemistry; Office of Advanced Cyberinfrastructure; School of Computer Science, Carnegie Mellon University; Mellon College of Science, Carnegie Mellon University; Genentech; Mitacs; Ontario Genomics Institute; National Institutes of Health; Ontario Genomics; Genome Canada; University of Ottawa; McGill University; National Cancer Institute; University of Toronto; Carnegie Mellon University; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Bayer; Bristol-Myers Squibb; Pfizer; National Science Foundation","keywords":"Affinities; In silico; Binding affinities; Chemistry; Ligand (biochemistry); Computational biology; Ligand efficiency; Leucine-rich repeat; Kinase; Binding site; Protein kinase A; Biochemistry; Stereochemistry; Combinatorial chemistry; Biology; Receptor; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009422041,0.00008810755,0.0001468424,0.0003027565,0.0001000179,0.0001883198,0.0002037066,0.00005080498,0.000001267239],"category_scores_gemma":[0.0006945576,0.00006629174,0.00006225523,0.0002676734,0.00001664468,0.001127601,0.0000556141,0.0002327514,2.482541e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001111736,"about_ca_system_score_gemma":0.0001552893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002787628,"about_ca_topic_score_gemma":2.107441e-7,"domain_scores_codex":[0.999005,0.00005444087,0.000533119,0.00007611864,0.0002214596,0.0001099312],"domain_scores_gemma":[0.9985315,0.0008350117,0.0002595075,0.00008316926,0.0002570366,0.00003374379],"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":[0.00009980262,0.00001448405,0.00001010108,0.00002308523,0.000008368177,3.293804e-7,0.0008755836,0.9863327,0.0004146638,0.003337008,0.00004575897,0.008838117],"study_design_scores_gemma":[0.0009705214,0.00003537217,0.000008565239,0.0001289543,0.000007707684,0.000004910519,0.0003706423,0.9879619,0.008241886,0.002084803,0.000118845,0.0000658268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2621478,0.00002279907,0.7354151,0.001922671,0.00009310624,0.0001011889,8.620498e-7,0.00001025177,0.0002862891],"genre_scores_gemma":[0.9174742,0.00002104361,0.08179706,0.000653711,0.00002942835,0.000008235274,0.000006301291,0.000002851055,0.000007218483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6553264,"threshold_uncertainty_score":0.2703299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03001461428771931,"score_gpt":0.3092174335169319,"score_spread":0.2792028192292126,"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."}}