Using alternative SMILES representations to identify novel functional analogues in chemical similarity vector searches

Clayton W Kosonocky, Aaron Lee Feller, Claus O Wilke, Andrew Ellington

Abstract:

Chemical similarity searches are a widely used family of in silico methods for identifying pharmaceutical leads.
These methods historically relied on structure-based comparisons to compute similarity. Here, we use a
chemical language model to create a vector-based chemical search. We extend previous 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 search results from nine
queries with diverse targets. We find that the method identifies molecules with similar patent-derived function-
ality to the query, as determined by our validated LLM-assisted patent summarization pipeline. Further, many
of these functionally similar molecules have different structures and scaffolds from the query, making them
unlikely to be found with traditional chemical similarity searches. This method may serve as a new tool for
the discovery of novel molecular structural classes that achieve target functionality.