  Recent advancements in generative models have established state-of-the-art
benchmarks in generating molecules and novel drug candidates. Despite these
successes, a significant gap persists between generative models and the
utilization of extensive biomedical knowledge, often systematized within
knowledge graphs, whose potential to inform and enhance generative processes
has not been realized. In this paper, we present a novel approach that bridges
this divide by developing a framework for knowledge-enhanced generative models
called K-DReAM. We develop a scalable methodology to extend the functionality
of knowledge graphs while preserving semantic integrity and incorporate this
contextual information into a generative framework to guide a diffusion-based
model. The integration of knowledge graph embeddings with our generative model
furnishes a robust mechanism for producing novel drug candidates possessing
specific characteristics while ensuring validity and synthesizability. K-DReAM
outperforms state-of-the-art generative models on both unconditional and
targeted generation tasks.
