  Natural language processing (NLP) is utilized in a wide range of fields,
where words in text are typically transformed into feature vectors called
embeddings. BioConceptVec is a specific example of embeddings tailored for
biology, trained on approximately 30 million PubMed abstracts using models such
as skip-gram. Generally, word embeddings are known to solve analogy tasks
through simple vector arithmetic. For instance, $\mathrm{\textit{king}} -
\mathrm{\textit{man}} + \mathrm{\textit{woman}}$ predicts
$\mathrm{\textit{queen}}$. In this study, we demonstrate that BioConceptVec
embeddings, along with our own embeddings trained on PubMed abstracts, contain
information about drug-gene relations and can predict target genes from a given
drug through analogy computations. We also show that categorizing drugs and
genes using biological pathways improves performance. Furthermore, we
illustrate that vectors derived from known relations in the past can predict
unknown future relations in datasets divided by year.
