  DNA encoded libraries (DELs) are used for rapid large-scale screening of
small molecules against a protein target. These combinatorial libraries are
built through several cycles of chemistry and DNA ligation, producing large
sets of DNA-tagged molecules. Training machine learning models on DEL data has
been shown to be effective at predicting molecules of interest dissimilar from
those in the original DEL. Machine learning chemical property prediction
approaches rely on the assumption that the property of interest is linked to a
single chemical structure. In the context of DNA-encoded libraries, this is
equivalent to assuming that every chemical reaction fully yields the desired
product. However, in practice, multi-step chemical synthesis sometimes
generates partial molecules. Each unique DNA tag in a DEL therefore corresponds
to a set of possible molecules. Here, we leverage reaction yield data to
enumerate the set of possible molecules corresponding to a given DNA tag. This
paper demonstrates that training a custom GNN on this richer dataset improves
accuracy and generalization performance.
