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

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neura...

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Veröffentlicht in:	Journal of chemical information and modeling 2024-02, Vol.64 (3), p.666-676
Hauptverfasser:	Zhang, Hao, Huang, Jinchao, Xie, Junjie, Huang, Weifeng, Yang, Yuedong, Xu, Mingyuan, Lei, Jinping, Chen, Hongming
Format:	Artikel
Sprache:	eng
Schlagworte:	Curricula Curriculum Design optimization Drug Design Drug Discovery Graph neural networks Lead compounds Learning Machine Learning and Deep Learning Molecular properties Neural Networks, Computer Similarity
Online-Zugang:	Volltext
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