{"id":"W4414162458","doi":"10.1021/acs.analchem.5c03103","title":"A Hit Prioritization Strategy for Compound Library Screening Using LiP-MS and Molecular Dynamics Simulations Applied to KRas G12D Inhibitors","year":2025,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"National Institutes of Health; Warren Y. Soper Charitable Trust; National Institute of General Medical Sciences; Jewish General Hospital; Canada Foundation for Innovation; Fondation De Famille Alvin Segal; Genome Canada; McGill University","keywords":"Cleavage (geology); Prioritization; KRAS; Proteolysis; Proteases; Molecular dynamics; Drug discovery; Ligand (biochemistry)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004094098,0.0006315518,0.000933771,0.0005999151,0.0004617667,0.0006246093,0.0006524884,0.0004267488,0.002294335],"category_scores_gemma":[0.0006608382,0.0003907804,0.0004792564,0.0003443455,0.0002948075,0.0004578963,0.0006797467,0.0004718953,0.0003178537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007731845,"about_ca_system_score_gemma":0.001358349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004195299,"about_ca_topic_score_gemma":0.004346625,"domain_scores_codex":[0.9998719,0.00002773452,0.000009844287,0.00001875899,0.00004360911,0.00002814738],"domain_scores_gemma":[0.9998281,0.00005375125,0.00002141948,0.00001419754,0.00004591458,0.00003661216],"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":[0.000546568,0.0002686809,0.002942645,0.0002140646,0.0001236537,0.0003673371,0.00007353175,0.8821625,0.06067664,0.009065117,0.001440324,0.04211904],"study_design_scores_gemma":[0.0000224039,0.00003513557,0.0001375081,0.000002636948,0.000009129901,0.00001215579,0.000005833615,0.9955322,0.00342409,0.0004314801,0.0003814052,0.000006051439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4351455,0.0008007417,0.5485508,0.0004964416,0.00009126487,0.000548643,0.0008619537,0.003549866,0.009954762],"genre_scores_gemma":[0.8088499,0.0003342549,0.1875898,0.0001557283,0.00001821526,0.0006351009,0.0005851197,0.0001847709,0.001646979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004195299,"threshold_uncertainty_score":0.008341789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220021462643201,"score_gpt":0.3215150975733042,"score_spread":0.2993148829468721,"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."}}