  Machine learning models have found numerous successful applications in
computational drug discovery. A large body of these models represents molecules
as sequences since molecular sequences are easily available, simple, and
informative. The sequence-based models often segment molecular sequences into
pieces called chemical words (analogous to the words that make up sentences in
human languages) and then apply advanced natural language processing techniques
for tasks such as $\textit{de novo}$ drug design, property prediction, and
binding affinity prediction. However, the chemical characteristics and
significance of these building blocks, chemical words, remain unexplored. This
study aims to investigate the chemical vocabularies generated by popular
subword tokenization algorithms, namely Byte Pair Encoding (BPE), WordPiece,
and Unigram, and identify key chemical words associated with protein-ligand
binding. To this end, we build a language-inspired pipeline that treats high
affinity ligands of protein targets as documents and selects key chemical words
making up those ligands based on tf-idf weighting. Further, we conduct case
studies on a number of protein families to analyze the impact of key chemical
words on binding. Through our analysis, we find that these key chemical words
are specific to protein targets and correspond to known pharmacophores and
functional groups. Our findings will help shed light on the chemistry captured
by the chemical words, and by machine learning models for drug discovery at
large.
