{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["26(5)"],"submitter":["Nishikiori K"],"pubmed_abstract":["In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coef"],"journal":["Molecular diversity"],"pagination":["2647-2657"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9532302"],"repository":["biostudies-literature"],"pubmed_title":["Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures."],"pmcid":["PMC9532302"],"pubmed_authors":["Uesawa Y","Nishikiori K","Tanaka K"],"additional_accession":[]},"is_claimable":false,"name":"Construction of a prediction model for drug removal rate in hemodialysis based on chemical structures.","description":"In designing drug dosing for hemodialysis patients, the removal rate (RR) of the drug by hemodialysis is important. However, acquiring the RR is difficult, and there is a need for an estimation method that can be used in clinical settings. In this study, the RR predictive model was constructed using the RR of known drugs by quantitative structure-activity relationship (QSAR) analysis. Drugs were divided into a model construction drug set (75%) and a model validation drug set (25%). The RR was collected from 143 medicines. The objective variable (RR) and chemical structural characteristics (descriptors) of the drug (explanatory variable) were used to construct a prediction model using partial least squares (PLS) regression and artificial neural network (ANN) analyses. The determination coef","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2026-04-28T03:19:07.543Z","creation":"2025-04-04T13:46:29.312Z"},"accession":"S-EPMC9532302","cross_references":{"pubmed":["34973116"],"doi":["10.1007/s11030-021-10348-7"]}}