  Artificial intelligence (AI) is increasingly used in every stage of drug
development. Continuing breakthroughs in AI-based methods for drug discovery
require the creation, improvement, and refinement of drug discovery data. We
posit a new data challenge that slows the advancement of drug discovery AI:
datasets are often collected independently from each other, often with little
overlap, creating data sparsity. Data sparsity makes data curation difficult
for researchers looking to answer key research questions requiring values posed
across multiple datasets. We propose a novel diffusion GNN model Syngand
capable of generating ligand and pharmacokinetic data end-to-end. We show and
provide a methodology for sampling pharmacokinetic data for existing ligands
using our Syngand model. We show the initial promising results on the efficacy
of the Syngand-generated synthetic target property data on downstream
regression tasks with AqSolDB, LD50, and hERG central. Using our proposed model
and methodology, researchers can easily generate synthetic ligand data to help
them explore research questions that require data spanning multiple datasets.
