Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model
This work addresses the problem of generating valid and synthesizable drug candidates with specific characteristics for drug discovery, representing a novel method for a known bottleneck.
The paper tackled the gap between generative models and biomedical knowledge graphs in molecule generation and drug discovery by developing KARL, a knowledge-enhanced generative model that integrates knowledge graph embeddings into a diffusion-based framework, resulting in outperforming state-of-the-art models on unconditional and targeted generation tasks.
Recent advancements in generative models have established state-of-the-art benchmarks in the generation of 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 KARL. 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. KARL outperforms state-of-the-art generative models on both unconditional and targeted generation tasks.