I have demonstrated a proof-of-concept for a foundational chemistry model, capable of predicting toxicity, palladium catalyzed cross coupling yields, and molecule fragrance from the 2D structures of these compounds. Key to this success was the utilization of a subset of the CCDC's dataset to generate a foundational model with enough chemical knowledge to be applicable across a range of chemistry fields. This was showcased with the development of three chemistry specific tasks, which outperformed baselines and other deep learning model benchmarks. The foundational model as well as the trained models are offered to the public for future exploration of toxicity, reaction outcome, and olfactive predictive modeling as well as other chemistry-relevant tasks such as structure activity relationship exploration (SAR) and reagent design.