The role of Artificial Intelligence (AI) is growing in every stage of drug development. Nevertheless, a major challenge in drug discovery AI remains: Drug pharmacokinetic (PK) datasets collected in different studies often exhibit limited overlap, creating data overlap sparsity. Thus, data curation becomes difficult, negatively impacting downstream research investigations in high-throughput screening, poly-pharmacy, and drug combination. We propose Imagand, a novel SMILES-to-Pharmacokinetic (S2PK) diffusion model capable of generating an array of PK target properties conditioned on SMILES inputs that exhibit data overlap sparsity. We show that Imagand-generated synthetic PK data closely resembles real data univariate and bivariate distributions, and can adequately fill in gaps among PK datasets. As such, Imagand is a promising solution for data overlap sparsity and may improve performance for downstream drug discovery research tasks.