MolDecor: Leveraging Transformers to Decorate Bioactive Molecules for Property Optimization

Lead
optimization is a critical stage in drug discovery, where
promising molecules (lead molecules) are further optimized. It involves
the refinement of the chemical structure of the lead molecule to improve
its pharmacological properties and drug-like characteristics for development
into potential therapies. In this study, we developed a pipeline that
includes (a) the creation of a property-specific fragment (decorator)
library, (b) learning fragment–scaffold relationship using
a BERT-based transformer model, and (c) decorating a given scaffold
using fragments from the generated fragment library for improving
the properties of the lead molecule. This transformer-based model,
MolDecor (Molecule Decorator), was trained on drug-like molecules
to learn the optimal decorators for property optimization at single
or multiple attachment points on the main scaffold of the lead molecule.
The model was fine-tuned on specific property data sets like solubility
and affinity using transfer learning to optimize these properties.
In this study, an automated method was developed to generate a property-specific
decorator library. By learning the relationship between scaffolds
and decorators, the model avoids bias toward the most commonly used
decorators. This also ensures the easy synthesizability of the generated
molecules. The model was tested on the anticancer drug (Thalidomide),
an antimalarial molecule (Compound 2), and the estrogen receptor modulator
(Cyclofenil) to enhance solubility. Additionally, the model was applied
to optimize the affinities of molecules targeting Janus kinase 1.