Recently, deep learning based molecular generative model has shown significant advancement and have been applied on fragment linking which could be used for fragment-based drug design (FBDD) and PROTAC design. For example, SyntaLinker , a SMILES based linker generative model based on the well-known transformer architecture, was recently been proposed to design linker to fuse two terminal fragments into a complete molecule. Zheng et al. recently has applied a similar methodology on PROTAC design. To overcome the deficiency of PROTAC molecules for model training, a large collection of quasi-PROTAC molecules that have similar characteristics to PROTACs was used to pre-train a prior model, which was then finetuned by using actual PROTACs augmented with randomized SMILES of fragments. Moreover, they utilized reinforcement learning (RL) to optimize the model to generate PROTACs with potentially better pharmacokinetic properties. This model showed superior performance on PROTAC generation over other linker generative models. In contrast, Delinker 21 is a graph-based 2D linker generative model and it was claimed that it could be used to design PROTAC molecules. Igashov et al proposed Difflinker model by integrating diffusion model with information of protein pocket for linker generation, and multiple fragments can be assembled into a complete molecule. Guo et al. reported Link-INVENT, a 2D linker generative model which demonstrated potential to generate both small molecules that conform to Lipinski "rule of five" 24 and PROTACs. The algorithm was essentially an expansion of the widely used REINVENT model incorporating a reward function to optimize the length, linearity and flexibility of generated linkers.