Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model

arXiv:2402.08790v1 Announce Type: new
Abstract: 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 …

abstract art arxiv benchmarks biomedical cs.lg discovery drug discovery gap generative generative models graphs knowledge knowledge graphs molecules novel q-bio.qm state type