GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning
Hao Zhang,
Jinchao Huang,
Junjie Xie
et al.
Abstract: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 neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synt… Show more
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