Gex2SGen: Designing Drug-like Molecules from Desired Gene Expression Signatures

Drug-induced gene expression profiling provides a lot
of useful
information covering various aspects of drug discovery and development.
Most importantly, this knowledge can be used to discover drugs’
mechanisms of action. Recently, deep learning-based drug design methods
are in the spotlight due to their ability to explore huge chemical
space and design property-optimized target-specific drug molecules.
Recent advances in accessibility of open-source drug-induced transcriptomic
data along with the ability of deep learning algorithms to understand
hidden patterns have opened opportunities for designing drug molecules
based on desired gene expression signatures. In this study, we propose
a deep learning model, Gex2SGen (Gene Expression 2 SMILES Generation),
to generate novel drug-like molecules based on desired gene expression
profiles. The model accepts desired gene expression profiles in a
cell-specific manner as input and designs drug-like molecules which
can elicit the required transcriptomic profile. The model was first
tested against individual gene-knocked-out transcriptomic profiles,
where the newly designed molecules showed high similarity with known
inhibitors of the knocked-out target genes. The model was next applied
on a triple negative breast cancer signature profile, where it could
generate novel molecules, highly similar to known anti-breast cancer
drugs. Overall, this work provides a generalized method, where the
method first learned the molecular signature of a given cell due to
a specific condition, and designs new small molecules with drug-like
properties.