Development of Simple and Accurate in Silico Ligand-Based Models for Predicting ABCG2 Inhibition.

ABSTRACT: The ATP binding cassette transporter ABCG2 is a physiologically important drug transporter that has a central role in determining the ADMET (absorption, distribution, metabolism, elimination, and toxicity) profile of therapeutics, and contributes to multidrug resistance. Thus, development of predictive in silico models for the identification of ABCG2 inhibitors is of great interest in the early stage of drug discovery. In this work, by exploiting a large public dataset, a number of ligand-based classification models were developed using partial least squares-discriminant analysis (PLS-DA) with molecular interaction field- and fingerprint-based structural description methods, regarding physicochemical and fragmental properties related to ABCG2 inhibition. An in-house dataset compiled from r

SUBMITTER: Huang S 

PROVIDER: S-EPMC9159808 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature