Literature DB >> 30918935

PgpRules: a decision tree based prediction server for P-glycoprotein substrates and inhibitors.

Pei-Hua Wang1, Yi-Shu Tu1, Yufeng J Tseng1,2.   

Abstract

SUMMARY: P-glycoprotein (P-gp) is a member of ABC transporter family that actively pumps xenobiotics out of cells to protect organisms from toxic compounds. P-gp substrates can be easily pumped out of the cells to reduce their absorption; conversely P-gp inhibitors can reduce such pumping activity. Hence, it is crucial to know if a drug is a P-gp substrate or inhibitor in view of pharmacokinetics. Here we present PgpRules, an online P-gp substrate and P-gp inhibitor prediction server with ruled-sets. The two models were built using classification and regression tree algorithm. For each compound uploaded, PgpRules not only predicts whether the compound is a P-gp substrate or a P-gp inhibitor, but also provides the rules containing chemical structural features for further structural optimization.
AVAILABILITY AND IMPLEMENTATION: PgpRules is freely accessible at https://pgprules.cmdm.tw/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2019        PMID: 30918935     DOI: 10.1093/bioinformatics/btz213

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  3 in total

1.  i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification.

Authors:  Minchao Jiang; Renfeng Zhang; Yixiao Xia; Gangyong Jia; Yuyu Yin; Pu Wang; Jian Wu; Ruiquan Ge
Journal:  Front Genet       Date:  2022-04-27       Impact factor: 4.772

2.  Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power.

Authors:  Tzu-Hui Yu; Bo-Han Su; Leo Chander Battalora; Sin Liu; Yufeng Jane Tseng
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

Review 3.  Machine learning models for classification tasks related to drug safety.

Authors:  Anita Rácz; Dávid Bajusz; Ramón Alain Miranda-Quintana; Károly Héberger
Journal:  Mol Divers       Date:  2021-06-10       Impact factor: 3.364

  3 in total

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