  Identification of high affinity drug-target interactions is a major research
question in drug discovery. Proteins are generally represented by their
structures or sequences. However, structures are available only for a small
subset of biomolecules and sequence similarity is not always correlated with
functional similarity. We propose ChemBoost, a chemical language based approach
for affinity prediction using SMILES syntax. We hypothesize that SMILES is a
codified language and ligands are documents composed of chemical words. These
documents can be used to learn chemical word vectors that represent words in
similar contexts with similar vectors. In ChemBoost, the ligands are
represented via chemical word embeddings, while the proteins are represented
through sequence-based features and/or chemical words of their ligands. Our aim
is to process the patterns in SMILES as a language to predict protein-ligand
affinity, even when we cannot infer the function from the sequence. We used
eXtreme Gradient Boosting to predict protein-ligand affinities in KIBA and
BindingDB data sets. ChemBoost was able to predict drug-target binding affinity
as well as or better than state-of-the-art machine learning systems. When
powered with ligand-centric representations, ChemBoost was more robust to the
changes in protein sequence similarity and successfully captured the
interactions between a protein and a ligand, even if the protein has low
sequence similarity to the known targets of the ligand.
