{"id":"W4390105536","doi":"10.26434/chemrxiv-2023-lnzvr","title":"In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Metal complexes synthesis and properties","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of British Columbia","funders":"Division of Chemistry; National Institutes of Health; National Science Foundation","keywords":"Virtual screening; In silico; Computational biology; Docking (animal); LRRK2; Computer science; Drug discovery; Druggability; Small molecule; Molecular dynamics; Chemical space; Cheminformatics; Chemistry; Bioinformatics; Computational chemistry; Biology; Biochemistry; Mutation; Medicine","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":[0.0003378679,0.0002445737,0.0006533267,0.000341333,0.00006526215,0.00002952857,0.0001505417,0.0002333236,0.00008776901],"category_scores_gemma":[0.0003412539,0.0002207716,0.0001374564,0.0001943875,0.0001154113,0.00005935793,0.0006934042,0.0002811137,0.000001882723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004932858,"about_ca_system_score_gemma":0.00006408167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008818608,"about_ca_topic_score_gemma":0.0001741769,"domain_scores_codex":[0.9984693,0.00007589274,0.0005627787,0.0004067199,0.0002560748,0.0002292333],"domain_scores_gemma":[0.9988511,0.0002273282,0.0002170048,0.0005441499,0.00007657384,0.00008380203],"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":[0.0003075508,0.0001883814,0.06448945,0.00293939,0.0004569007,0.00008149471,0.002641576,0.03228061,0.8890024,0.001363929,0.0002835357,0.005964776],"study_design_scores_gemma":[0.003317538,0.0001696132,0.03046421,0.009629487,0.0006451706,0.00005244905,0.001404083,0.2732705,0.6338947,0.04128164,0.004351826,0.00151881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951315,0.001610945,0.001931344,0.0003280551,0.0001977711,0.0001924179,0.000004645141,0.00004530298,0.0005580468],"genre_scores_gemma":[0.9911301,0.00007371404,0.008186591,0.00006096639,0.0002700165,0.00001228532,0.00002354771,0.00005481863,0.0001879508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2551077,"threshold_uncertainty_score":0.9002805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093283096937162,"score_gpt":0.3082503565092996,"score_spread":0.1989220468155834,"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."}}