Previously developed computational tools for linker design involve searching a database, making the generalizability of proposed linkers inherently limited. While success has been demonstrated when using these methods combined with filtering steps, one would ideally want to generalize the task such that plausible linker ideas can be proposed given any molecular subunits. Recently, DL-based linker design models have been proposed that circumvent database searches. DeLinker is a graphbased model proposed by Imrie et al. which explicitly incorporates 3D information via the distance and angle between the molecular subunits to augment the feature vector. SyntaLinker is a conditional transformer model proposed by Yang et al. which treats linker generation as a natural language processing (NLP) task using SMILES. SyntaLinker was further extended by Hu et al. to perform kinase scaffold hopping after focusing the model via transfer learning. Moreover, Langevin et al. proposed the Scaffold Constrained Molecular Generation (SAMOA) algorithm based on recurrent neural networks (RNNs) where one of the capabilities of the model is linker generation. However, while these models are capable of generating linker ideas, a major drawback is the limited support to optimize explicitly for desired physico-chemical properties. The current models only allow users to control for the desired linker length and a select number of physico-chemical properties, e.g., number of hydrogen-bond donors (HBD). To encourage wide adoption of DL-based linker design, increased flexibility to define tailored MPO objectives and better generalizability is needed.