Doddareddy, Munikumar R. Cho, Yong Seo Koh, Hun Yeong Kim, Dong Hyun Pae, Ae Nim 2024-01-21T03:05:29Z 2024-01-21T03:05:29Z 2021-09-01 2006-05 1549-9596 https://pubs.kist.re.kr/handle/201004/135562 A data set of 130 diverse compounds containing both central nervous system (CNS) and non-CNS drugs was used to generate a renal clearance model using a classical Volsurf approach. Percentage renal clearance data was used as a biological input. The score plots obtained from principal component analysis and partial least-squares (PLS) analysis clearly separated high-clearance compounds from low-clearance compounds. PLS models were used to predict the renal clearance of the data set. Categorical statistical methods such as SIMCA and recursive partitioning techniques were used for classifying the compounds into low-and high-clearance categories. PLS coefficient plots, Volsurf descriptor profiles, 3D Grid maps, and RP decision trees were used to explain the important descriptors separating low and high renal clearance compounds. For comparative purposes, topological descriptors such as Molconn-Z were also examined. All the models were validated by an external test set of 20 compounds. These models can be used as efficient tools in the classification and prediction of the renal clearance of unknown compounds, the knowledge of which is helpful in understanding their bioavailability behavior. English AMER CHEMICAL SOC PERMEATION PARAMETERS ABSORPTION In silico renal clearance model using classical Volsurf approach Article 10.1021/ci0503309 1 JOURNAL OF CHEMICAL INFORMATION AND MODELING, v.46, no.3, pp.1312 - 1320 JOURNAL OF CHEMICAL INFORMATION AND MODELING 46 3 1312 1320 scie scopus 000237658400041 2-s2.0-33745398350 Chemistry, Medicinal Chemistry, Multidisciplinary Computer Science, Information Systems Computer Science, Interdisciplinary Applications Pharmacology & Pharmacy Chemistry Computer Science Article PERMEATION PARAMETERS ABSORPTION chemoinformatics in silico renal clearance modeling