Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks-Reference-Cited by-同舟云学术

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Development of predictive QSAR models for the substrates/inhibitors of OATP1B1 by deep neural networks

Published:2023-03 Issue: Volume:376 Page:20-25
ISSN:0378-4274
Container-title:Toxicology Letters
language:en
Short-container-title:Toxicology Letters

Author:

Gui Chunshan,Li Ying,Peng Taotao

Funder

National Natural Science Foundation of China

Priority Academic Program Development of Jiangsu Higher Education Institutions

Publisher

Elsevier BV

Subject

Toxicology,General Medicine

Reference47 articles.

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5. In silico assessment of chemical biodegradability;Cheng;J. Chem. Inf. Model,2012

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1. In silico screening of Fyn kinase inhibitors using classification-based QSAR model, molecular docking, molecular dynamics and ADME study;Molecular Diversity;2024-06-17

2. Identification of potential FAK inhibitors using mol2vec molecular descriptor-based QSAR, molecular docking, ADMET study, and molecular dynamics simulation;Molecular Diversity;2024-04-06

3. In Silico prediction of inhibitors for multiple transporters via machine learning methods;Molecular Informatics;2024-02-06

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