{"id":"W4393276817","doi":"10.26434/chemrxiv-2023-lnzvr-v2","title":"In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Cancer therapeutics and mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"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":"In silico; Docking (animal); Computational biology; Computer science; Chemistry; Medicine; Biology; Biochemistry","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":[0.0007893434,0.001233571,0.001944483,0.0005582649,0.0004756527,0.0007163733,0.001231382,0.0007960189,0.001340852],"category_scores_gemma":[0.001031303,0.000484359,0.001040711,0.0006062066,0.000385084,0.0004882154,0.0009344153,0.0009590504,0.0002358528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001034278,"about_ca_system_score_gemma":0.001496825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005794324,"about_ca_topic_score_gemma":0.006907098,"domain_scores_codex":[0.9996731,0.00009575166,0.0000195517,0.00004228,0.0001107308,0.00005861561],"domain_scores_gemma":[0.999564,0.0002449888,0.00003693656,0.00004407972,0.00005908297,0.00005091211],"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":[0.0001681865,0.0001829316,0.001048141,0.00008849479,0.00007932909,0.00008214112,0.00001590612,0.9844124,0.006883784,0.001641302,0.0004727676,0.004924592],"study_design_scores_gemma":[0.0000533196,0.000184541,0.0001729971,0.000002859094,0.00001173543,0.000012737,0.000008051838,0.9958879,0.002999116,0.0004329905,0.0002258679,0.000007881626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9360362,0.0007273033,0.05026308,0.0003449972,0.00006153662,0.0002186292,0.001336895,0.001456312,0.009555127],"genre_scores_gemma":[0.9546563,0.0004094058,0.04213972,0.0001417235,0.00001590204,0.000336566,0.001160441,0.0001034681,0.001036557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005794324,"threshold_uncertainty_score":0.01152116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01877879689786189,"score_gpt":0.2768646652048879,"score_spread":0.2580858683070261,"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."}}