Model-Based Virtual PK/PD Exploration and Machine Learning Approach to Define PK Drivers in Early Drug Discovery-Reference-Cited by-同舟云学术

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Model-Based Virtual PK/PD Exploration and Machine Learning Approach to Define PK Drivers in Early Drug Discovery

Published:2024-02-20 Issue:5 Volume:67 Page:3727-3740
ISSN:0022-2623
Container-title:Journal of Medicinal Chemistry
language:en
Short-container-title:J. Med. Chem.

Author:

Chen Emile P.¹^ORCID,Dutta Shayoni¹,Ho Ming-Hsun²,DeMartino Michael P.³^ORCID

Affiliation:

1. Systems Modeling and Translational Biology, Computational Sciences, GSK, Collegeville, Pennsylvania 19426, United States

2. Molecular Design, Computational Sciences, GSK, Collegeville, Pennsylvania 19426, United States

3. Medicinal Chemistry, GSK, Collegeville, Pennsylvania 19426, United States

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jmedchem.3c02169

Reference30 articles.

1. Model-based Target Pharmacology Assessment (mTPA): An Approach Using PBPK/PD Modeling and Machine Learning to Design Medicinal Chemistry and DMPK Strategies in Early Drug Discovery

2. Inverse‐QSPR for de novo Design: A Review

3. Applications of Model-Based Target Pharmacology Assessment in Defining Drug Design and DMPK Strategies: GSK Experiences

4. Classification And Regression Trees

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity;Clinical Pharmacokinetics;2024-09-03

2. DrugGym: A testbed for the economics of autonomous drug discovery;2024-06-02

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