Prompt Engineering for Transformer-based Chemical Similarity Search
  Identifies Structurally Distinct Functional Analogues
Clayton W. Kosonocky Aaron L. Feller Claus O. Wilke Andrew D. Ellington
Chemical similarity searches are widely used in-silico methods for
identifying new drug-like molecules. These methods have historically relied on
structure-based comparisons to compute molecular similarity. Here, we use a
chemical language model to create a vector-based chemical search. We extend
implementations by creating a prompt engineering strategy that utilizes two
different chemical string representation algorithms: one for the query and the
other for the database. We explore this method by reviewing the search results
from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine,
lysergic acid diethylamide, and fentanyl) and three dye-like query molecules
(acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this
novel method identifies molecules that are functionally similar to the query,
indicated by the associated patent literature, and that many of these molecules
are structurally distinct from the query, making them unlikely to be found with
traditional chemical similarity search methods. This method may aid in the
discovery of novel structural classes of molecules that achieve target
functionality.