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.