Here I report the development of a general chemistry-centric foundational model. Approximately 1 million experimentally validated organic crystal structures from the Cambridge Crystallographic Data Centre (CCDC) was used to train this foundational model. The key hypothesis is that that if a ML model was capable of predicting accurate crystal structure information, its latent space would be useful in the prediction of many other chemical outcomes. It was envisioned that the size and scope of the CCDC would allow for a deep neural network approach, capable of inferring nuanced interactions between atoms and/or motifs from a given molecule, to facilitate rapid specialization. Importantly, as only the final output block of the neural network would be undergoing training, this would yield a modular, flexible framework capable of reliable prediction even on limited training data. The modularity and accuracy of this approach on 3 chemistry-related prediction tasks: toxicity prediction, yield prediction, and olfaction prediction are reported, showcasing a performance improvement over other documented ML techniques and, when applicable, comparison to other deep learning models (Figure ).