GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning-Reference-Cited by-同舟云学术

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GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Published:2024-01-19 Issue:3 Volume:64 Page:666-676
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zhang Hao¹^ORCID,Huang Jinchao¹,Xie Junjie²^ORCID,Huang Weifeng¹,Yang Yuedong²^ORCID,Xu Mingyuan³^ORCID,Lei Jinping¹^ORCID,Chen Hongming³

Affiliation:

1. School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China

2. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China

3. Guangzhou National Laboratory, Guangzhou International Bio Island, No. 9 Xin Dao Huan Bei Road, Guangzhou 510005, China

Funder

National Key Research and Development Program of China

Special Project for Research and Development in Key areas of Guangdong Province

National Natural Science Foundation of China

Guangdong-Hong Kong Technology Cooperation Funding Scheme

Pearl River Recruitment Program of Talents

Guangzhou Basic and Applied Basic Research Project

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.3c01700

Reference56 articles.

1. Twenty years on: the impact of fragments on drug discovery

2. Concepts and Core Principles of Fragment-Based Drug Design

3. Current perspectives in fragment-based lead discovery (FBLD)

4. Fragment Informatics and Computational Fragment-Based Drug Design: An Overview and Update

5. Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications

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