  Recent research on predicting the binding affinity between drug molecules and
proteins use representations learned, through unsupervised learning techniques,
from large databases of molecule SMILES and protein sequences. While these
representations have significantly enhanced the predictions, they are usually
based on a limited set of modalities, and they do not exploit available
knowledge about existing relations among molecules and proteins. In this study,
we demonstrate that by incorporating knowledge graphs from diverse sources and
modalities into the sequences or SMILES representation, we can further enrich
the representation and achieve state-of-the-art results for drug-target binding
affinity prediction in the established Therapeutic Data Commons (TDC)
benchmarks. We release a set of multimodal knowledge graphs, integrating data
from seven public data sources, and containing over 30 million triples. Our
intention is to foster additional research to explore how multimodal knowledge
enhanced protein/molecule embeddings can improve prediction tasks, including
prediction of binding affinity. We also release some pretrained models learned
from our multimodal knowledge graphs, along with source code for running
standard benchmark tasks for prediction of biding affinity.
