{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000613322,0.001036703,0.001624207,0.0006180239,0.0004894485,0.0006094528,0.00106539,0.0007439832,0.002022052],"category_scores_gemma":[0.0009269945,0.0004586009,0.0007477711,0.0007031109,0.0003763122,0.0004464858,0.0008049303,0.0007137573,0.0002767014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009184307,"about_ca_system_score_gemma":0.001185437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005010446,"about_ca_topic_score_gemma":0.0050144,"domain_scores_codex":[0.9997644,0.00006644284,0.00001416432,0.00003223862,0.00007409762,0.00004864694],"domain_scores_gemma":[0.9996482,0.0002014582,0.00002858069,0.00003879837,0.00004583702,0.0000370763],"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":[0.0001839184,0.0001366213,0.0008712651,0.0001252253,0.0000767094,0.0001037162,0.00002338704,0.9823024,0.007832536,0.002906522,0.0005852491,0.004852599],"study_design_scores_gemma":[0.0000515093,0.00009641604,0.0001550662,0.000003332639,0.00001171467,0.00001224395,0.000008964275,0.9953054,0.00334949,0.0007231808,0.000276133,0.000006501176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9100532,0.0008029011,0.06915581,0.000400776,0.00006373426,0.0001827028,0.00168095,0.001552271,0.01610769],"genre_scores_gemma":[0.9588686,0.0003989437,0.03790987,0.0001181295,0.00001360439,0.0002985279,0.001034127,0.0001349418,0.001223283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005010446,"threshold_uncertainty_score":0.009962559,"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."}}